In this paper, we present our recent work on cells detection and segmentation in estrogen recepto... more In this paper, we present our recent work on cells detection and segmentation in estrogen receptor immunohistochemistry (ER-IHC)-stained breast carcinoma images. The proposed cell detection and segmentation is very useful in the predictive scoring of hormone receptor status in ER-IHC stained whole-slide images, which helps pathologists to decide whether a patient should be offered hormonal therapy or other treatments. The proposed method is based on deep convolutional neural network, followed by watershed-based post-processing. The cell detection results are compared and evaluated objectively against the ground truth provided by our collaborating pathologists. The cell segmentation results, on the other hand, are evaluated visually by overlaying the computer segmented boundaries on the ER-IHC images for comparison. The automated cell detection algorithm recorded precision and recall rates of 95% and 91% respectively. The very promising performances for both the detection and segmentation paves the way for an automated system for hormone receptor scoring in ER-IHC stained whole-slide breast carcinoma images.
The advent of multimedia technology has offer new dimension to computerized applications. Art-bas... more The advent of multimedia technology has offer new dimension to computerized applications. Art-based applications are among those which have and will continue to benefit from this advancement. With the ever growing size and variety of accessible data across museum collections, the need for flexible and efficient data retrieval is growing at an alarming rate. Content-based image retrieval (CBIR) and analysis is getting a lot of attention from museums and art institutions. One of the image-based requirements from museums, is to automatically classify craquelure (cracks) in paintings for the purpose of aiding damage assessment. Craquelure in paintings can be an important element in judging authenticity, use of material as well as environmental and physical impact because these can lead to different craquelure patterns. Mass screening of craquelure patterns will help to establish a better platform for conservators to identify cause of damage. As a way of performing such action, a content...
In this paper, we present a 2-stage approach to connected curve grouping. The algorithm is experi... more In this paper, we present a 2-stage approach to connected curve grouping. The algorithm is experimented and demonstrated on crack-detected images of paintings. Some features are left undetected and this tends to produce disconnected curves. In order to extract high-level features for content-based application, these supposedly connected curves have to be grouped together. It is one of the many steps needed to produce a content-based platform for digital analysis of crack patterns in paintings particularly for classification purpose. The prime objective of the grouping algorithm is to segment or partition areas of an image to produce reliable representations of content. The first stage of the algorithm utilizes the Minimum Bounding Rectangle (MBR) of a crack network as means of finding overlapping features. We demonstrate the use of the both the rotated and the un-rotated MBR. In the second stage, curve characteristics represented by the rotated MBR such as the dimension ratio, the a...
2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)
Human action recognition is one of the most challenging computer vision topics and an important r... more Human action recognition is one of the most challenging computer vision topics and an important realistic yet open research task in many relevant applications such as security, content retrieval and video surveillance. In this paper, we proposed a robust approach for human action recognition in different video scenes. The architecture of the approach consists of shot boundary detection, shot frame rate resampling, extracting compact feature vectors by emphasizing variation and classification. Extracting strong patterns in feature vectors before classification affect the chosen approach high performance compared with latest state-of-art work using four widely used realistic data sets: Hollywood-2, UCF11(YouTube actions), UCF Sports and KTH.
In this paper, we extended our previous non-adaptive human action recognition framework by incorp... more In this paper, we extended our previous non-adaptive human action recognition framework by incorporating an adaptive model in order to exhibit minimal supervision system where the intelligent system will handle the process of human action detection, labelling, training and recognition in automated manner. Action descriptor that is formed in non-adaptive model framework is compared with the stored action descriptors in order to determine a new human action. Then, the collected new action descriptors are clustered using agglomerative hierarchical clustering algorithm. Subsequently, all the clustered action descriptors are assigned with new labels and the multi-class SVM classifier is updated automatically. Additionally, we evaluated the adaptive model on public depth dataset namely MSR-Action 3D dataset. The experimental results reveal that the proposed adaptive framework is comparable with state of the arts methods which were developed in supervised manner. In future, conventional RG...
2020 2nd International Conference on Video, Signal and Image Processing, 2020
Recognition of human action is one of the challenges in the field of artificial intelligence. Dee... more Recognition of human action is one of the challenges in the field of artificial intelligence. Deep learning model has become a research issue in action recognition applications due to its ability to outperform traditional machine learning approaches. The Convolutional Neural Network is one of the architectures commonly used in most action recognition works. There are different models in the Convolutional Neural Network, but no study has been done to evaluate which model has the best performance in understanding human actions. Thus, in this paper, we compare the performance of two separate pre-trained models of deep Convolutional Neural Network in classifying the human actions to identify the different behaviours. GoogleNet and AlexNet are the used two models with fine-tuned parameters used for comparison, in addition, to use Long-Short Term Memory for the video's labels prediction. The paper's main contribution is that it offers a performance analysis of two separate fine-tu...
Electrocardiogram (ECG) signal compression is playing a vital role in biomedical applications. Th... more Electrocardiogram (ECG) signal compression is playing a vital role in biomedical applications. The signal compression is meant for detection and removing the redundant information from the ECG signal. Wavelet transform methods are very powerful tools for signal and image compression and decompression. This paper deals with the comparative study of ECG signal compression using preprocessing and without preprocessing approach on the ECG data. The performance and efficiency results are presented in terms of percent root mean square difference (PRD). Finally, the new PRD technique has been proposed for performance measurement and compared with the existing PRD technique; which has shown that proposed new PRD technique achieved minimum value of PRD with improved results.
2017 International Conference on Robotics, Automation and Sciences (ICORAS), 2017
The proposed method is a contrast measurement technique which makes use of the second-order deriv... more The proposed method is a contrast measurement technique which makes use of the second-order derivatives of the pixels in MRI images. The technique utilizes the changes in the histogram of second-order derivatives for the image, specifically the region under a Laplacian curve which approximates the distribution of the pixel frequencies in the histogram. As gain is introduced, the region of the histogram under the curve changes. The standard deviation of the frequency differences between the histogram and the curve in the region is used as a measurement score. The objective of this method is to provide a way to include small details, such as small blood vessels, in the contrast measurement, compared to local average contrast and RMS contrast measurements which do not. The outcome is a scoring method which not only provides a measurement of contrast, but also indicates the loss of small details in MRI images.
Histopathological analysis of tissues has been gaining a lot of interests recently, from developi... more Histopathological analysis of tissues has been gaining a lot of interests recently, from developing computer algorithms to assist pathologists from cell detection and counting, to tissue classification and cancer grading. With the advent of whole slide imaging, the field of digital pathology has gained enormous popularity, and is currently regarded as one of the most promising avenues of diagnostic medicine. Deep learning advancement on image set today has successfully evolved as many models has been proposed and produced state-of-the-art object classifying results. This is not limited to large database such as Imagenet but also has seen applications in other medical image analysis related areas. In this paper we have carefully constructed and expanded the deep model network to classify normal and tumor slides in histology images of lymph nodes tissue. We have proposed our own deep learning model based on convolutional neural network with smaller requirement using 64×64×3 input imag...
Locally Linear Embedding (LLE) is an unsupervised non-linear manifold learning method, which has ... more Locally Linear Embedding (LLE) is an unsupervised non-linear manifold learning method, which has spurred increased interest in face recognition research recently. However, it is commonly known that a supervised method that considering the class-specific information always outperforms the unsupervised one, especially in biometric recognition task. In this paper, we propose a supervised LLE technique, known as class-label Locally Linear Embedding (cLLE). cLLE aims to discover the nonlinearity of high-dimensional data by minimizing the global reconstruction error of the set of all local neighbours in the data set. cLLE method is using user class-specific information in neighbourhoods selection and thus preserves the local neighbourhoods. Since the locality preservation is correlated to the class discrimination, the proposed cLLE is expected superior to LLE in face recognition. Experimental results on three face databases demonstrate the success of the proposed technique.
In sports science, two widely used approaches to perform movement recognition and analysis are th... more In sports science, two widely used approaches to perform movement recognition and analysis are through manual annotation of sports video and physical body marker attached to athlete’s body. The use of physical body markers, however, requires expertise on visual annotation which is obviously time-consuming and inconvenient for the athletes. Badminton is one of Malaysia’s most popular sports but there is still a lack of scientific research on movement recognition and analysis focusing on this sport. Therefore, in this paper, a novel lossless compact view invariant compression technique with a dynamic time warping algorithm is proposed to cater for both badminton movement recognition and analysis frameworks. Our experimental dataset of depth map sequences composed of 10 types of badminton movements with a total of 600 samples performed by 20 badminton players. The dataset varies in terms of viewpoints, human body size, clothes, speed, and gender. Experimental results revealed that near...
With the increasing prevalence rate of diabetes and obesity worldwide, chronic wounds are becomin... more With the increasing prevalence rate of diabetes and obesity worldwide, chronic wounds are becoming a significant burden for world health and economy. The treatment of a chronic wound goes through complex and time-intensive process. During the healing period, continuous wound measurement helps clinicians to predict the healing time and monitor the treatment efficiency. Current clinical techniques such as ruler-based or tracing-based methods are inaccurate, time-consuming and also subject to intra- and inter-reader variability that does not satisfy a comprehensive clinical benchmark. In this paper, we proposed a method for wound boundary demarcation and estimation based on Super pixel segmentation and classification using an enhanced convolution neural network. An overall accuracy, sensitivity, and specificity of around 90% was observed, which fared much better against traditional methods.
2018 IEEE Student Conference on Research and Development (SCOReD), 2018
Chronic wound is becoming a major threat for world health and economy. In the USA alone, an estim... more Chronic wound is becoming a major threat for world health and economy. In the USA alone, an estimated 6.5 million people are affected by the chronic wound and the annual cost for chronic wound treatment is reportedly more than 25 billion dollars. The process of chronic wound healing is very complex and time-consuming. Quantification of wound size plays a vital role for clinical wound treatment as the physical dimension of a wound is an important clue for wound assessment. The current techniques for wound area measurement are the ruler method and tracing which is mainly based on visual inspection, thus are not very accurate as well as time-consuming. A computerized wound measurement system can provide a more accurate measurement, reduce bias and errors due to fatigue and can potentially reduce clinical workload. In this paper, we proposed a simple but efficient method for wound area segmentation based on superpixel classification with color and texture feature and SVM classifier. Som...
Monitoring the changes in wound size over time is one of the most important parameters for assess... more Monitoring the changes in wound size over time is one of the most important parameters for assessment of the applied treatment plan in wound care. The current manual methods for wound measurement have several drawbacks including high error rates, time consuming and also causing patient discomfort because of pain or infection. There are some computerized tools available in clinical settings known as digital planimetry which require the clinician to identify wound boundaries and calibrate them manually. However these methods are a barrier to achieving clinical quality benchmarks as the accuracy decreases drastically with improper camera lens orientation and some other factors. This paper proposes an automated calibration method for wound measurement based on the segmentation of reference label card and geometrical image operations. Due to the uncontrolled setting of the camera for our images, segmentation of the label card can be very challenging, thus preventing accurate calibration and wound size measurement. We proposed a label card segmentation method based on probability map approach and superpixel region growing. Theoretically, the white label card regions would have a distinguished probability map in the white channel compared to other regions, generating a reliable initial segmentation. Morphological operations and edge detection are then performed to obtain a more accurate final segmentation. Based on the segmented label card, the size of the pixel can be calibrated, and the size of the wound can be automatically calculated. Experimental results confirm the feasibility of the proposed method by demonstrating segmentation accuracy in terms of the Jaccard index of 94.77%, Dice coefficient of 97.20%, and contour matching score of 94.21%. The wound measurement accuracy also shows very promising results.
2021 IEEE 17th International Colloquium on Signal Processing & Its Applications (CSPA)
The presence of robots in our daily life is becoming more common, where robots start carrying out... more The presence of robots in our daily life is becoming more common, where robots start carrying out more complex tasks. This increase in the complexity of tasks makes conventional control system insufficient. Therefore, a plausible approach is required for robots to learn how to perform these tasks. Reinforcement learning enables robots to perform complex tasks without highly engineered control systems. However, using reinforcement learning in robotic applications is challenged by several problems such as high dimensionality. Thus, in this paper, we study the performance of the Hindsight Experience Replay (HER) algorithm which addresses the high dimensionality problem. In this paper, we analyze the algorithm performance using a simulated robotic arm to pick and place different objects. Then, we propose the use of vision feedback which is used to control the gripper of the robotic arm. The results and analysis highlights some of HER limitations when dealing with objects that have limited grasping points. Our proposed method allows the robotic arm to pick objects using the same trained policy without the need to retrain the agent for new objects. Finally, we prove that using our method the robotic arm can pick the objects with higher success rate compared to the one without vision feedback.
An individual with profound deafness or total hearing loss has a hearing threshold of 80dB or mor... more An individual with profound deafness or total hearing loss has a hearing threshold of 80dB or more. The ineffectiveness of hearing aids, surging costs and complex surgeries for cochlear implants have discouraged many to opt for these types of treatments. Hence, this research aims to provide an alternative hearing aid that stimulates “hearing” through the skin sensory, which is more affordable and accessible for the profoundly deaf or total hearing loss community. We have developed four initial vibrating transducers with single spectrum, which are strapped to a belt. The transducers pick up audible sounds through a microphone, amplifies the sound to a high-level signal, stimulating a vibration pattern on the human skin sensory. The belt was tested on 30 random people who identified as normal, partial, and profoundly deaf. When the belt was strapped to the individual’s waist, audible sound was played (stimulus) and the individual was asked whether he/she can feel a stimulation or vi...
International Journal of Pattern Recognition and Artificial Intelligence
A tremendous increase in the video content uploaded on the internet has made it necessary for aut... more A tremendous increase in the video content uploaded on the internet has made it necessary for auto-recognition of videos in order to analyze, moderate or categorize certain content that can be accessed easily later on. Video analysis requires the study of proficient methodologies at the semantic level in order to address the issues such as occlusions, changes in illumination, noise, etc. This paper is aimed at the analysis of the soccer videos and semantic processing as an application in the video semantic analysis field. This study proposes a framework for automatically generating and annotating the highlights from a soccer video. The proposed framework identifies the interesting clips containing possible scenes of interest, such as goals, penalty kicks, etc. by parsing and processing the audio/video components. The framework analyzes, separates and annotates the individual scenes inside the video clips and saves using kernel support vector machine. The results show that semantic a...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2018
Hormone receptor status in breast carcinoma is determined primarily to identify patients who may ... more Hormone receptor status in breast carcinoma is determined primarily to identify patients who may benefit from hormonal therapy. Estrogen receptor (ER) is one of the hormone receptor positive factors which have been recognized as a marker for which women with breast cancer would respond to hormone treatment. We propose a system to classify cells in ER-stained whole slide breast carcinoma images according to their staining strength using convolutional neural network (CNN). The proposed CNN multiclass classifier was tested on a region of 1200 cells, and achieved very promising results, with overall accuracy of 88.8% and AUC score of 97.5%. The proposed system is useful for use in hormone receptor testing, where the outcomes are used to decide whether the cancer is likely to respond to hormonal therapy or other treatments.
In this paper, we present our recent work on cells detection and segmentation in estrogen recepto... more In this paper, we present our recent work on cells detection and segmentation in estrogen receptor immunohistochemistry (ER-IHC)-stained breast carcinoma images. The proposed cell detection and segmentation is very useful in the predictive scoring of hormone receptor status in ER-IHC stained whole-slide images, which helps pathologists to decide whether a patient should be offered hormonal therapy or other treatments. The proposed method is based on deep convolutional neural network, followed by watershed-based post-processing. The cell detection results are compared and evaluated objectively against the ground truth provided by our collaborating pathologists. The cell segmentation results, on the other hand, are evaluated visually by overlaying the computer segmented boundaries on the ER-IHC images for comparison. The automated cell detection algorithm recorded precision and recall rates of 95% and 91% respectively. The very promising performances for both the detection and segmentation paves the way for an automated system for hormone receptor scoring in ER-IHC stained whole-slide breast carcinoma images.
The advent of multimedia technology has offer new dimension to computerized applications. Art-bas... more The advent of multimedia technology has offer new dimension to computerized applications. Art-based applications are among those which have and will continue to benefit from this advancement. With the ever growing size and variety of accessible data across museum collections, the need for flexible and efficient data retrieval is growing at an alarming rate. Content-based image retrieval (CBIR) and analysis is getting a lot of attention from museums and art institutions. One of the image-based requirements from museums, is to automatically classify craquelure (cracks) in paintings for the purpose of aiding damage assessment. Craquelure in paintings can be an important element in judging authenticity, use of material as well as environmental and physical impact because these can lead to different craquelure patterns. Mass screening of craquelure patterns will help to establish a better platform for conservators to identify cause of damage. As a way of performing such action, a content...
In this paper, we present a 2-stage approach to connected curve grouping. The algorithm is experi... more In this paper, we present a 2-stage approach to connected curve grouping. The algorithm is experimented and demonstrated on crack-detected images of paintings. Some features are left undetected and this tends to produce disconnected curves. In order to extract high-level features for content-based application, these supposedly connected curves have to be grouped together. It is one of the many steps needed to produce a content-based platform for digital analysis of crack patterns in paintings particularly for classification purpose. The prime objective of the grouping algorithm is to segment or partition areas of an image to produce reliable representations of content. The first stage of the algorithm utilizes the Minimum Bounding Rectangle (MBR) of a crack network as means of finding overlapping features. We demonstrate the use of the both the rotated and the un-rotated MBR. In the second stage, curve characteristics represented by the rotated MBR such as the dimension ratio, the a...
2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)
Human action recognition is one of the most challenging computer vision topics and an important r... more Human action recognition is one of the most challenging computer vision topics and an important realistic yet open research task in many relevant applications such as security, content retrieval and video surveillance. In this paper, we proposed a robust approach for human action recognition in different video scenes. The architecture of the approach consists of shot boundary detection, shot frame rate resampling, extracting compact feature vectors by emphasizing variation and classification. Extracting strong patterns in feature vectors before classification affect the chosen approach high performance compared with latest state-of-art work using four widely used realistic data sets: Hollywood-2, UCF11(YouTube actions), UCF Sports and KTH.
In this paper, we extended our previous non-adaptive human action recognition framework by incorp... more In this paper, we extended our previous non-adaptive human action recognition framework by incorporating an adaptive model in order to exhibit minimal supervision system where the intelligent system will handle the process of human action detection, labelling, training and recognition in automated manner. Action descriptor that is formed in non-adaptive model framework is compared with the stored action descriptors in order to determine a new human action. Then, the collected new action descriptors are clustered using agglomerative hierarchical clustering algorithm. Subsequently, all the clustered action descriptors are assigned with new labels and the multi-class SVM classifier is updated automatically. Additionally, we evaluated the adaptive model on public depth dataset namely MSR-Action 3D dataset. The experimental results reveal that the proposed adaptive framework is comparable with state of the arts methods which were developed in supervised manner. In future, conventional RG...
2020 2nd International Conference on Video, Signal and Image Processing, 2020
Recognition of human action is one of the challenges in the field of artificial intelligence. Dee... more Recognition of human action is one of the challenges in the field of artificial intelligence. Deep learning model has become a research issue in action recognition applications due to its ability to outperform traditional machine learning approaches. The Convolutional Neural Network is one of the architectures commonly used in most action recognition works. There are different models in the Convolutional Neural Network, but no study has been done to evaluate which model has the best performance in understanding human actions. Thus, in this paper, we compare the performance of two separate pre-trained models of deep Convolutional Neural Network in classifying the human actions to identify the different behaviours. GoogleNet and AlexNet are the used two models with fine-tuned parameters used for comparison, in addition, to use Long-Short Term Memory for the video's labels prediction. The paper's main contribution is that it offers a performance analysis of two separate fine-tu...
Electrocardiogram (ECG) signal compression is playing a vital role in biomedical applications. Th... more Electrocardiogram (ECG) signal compression is playing a vital role in biomedical applications. The signal compression is meant for detection and removing the redundant information from the ECG signal. Wavelet transform methods are very powerful tools for signal and image compression and decompression. This paper deals with the comparative study of ECG signal compression using preprocessing and without preprocessing approach on the ECG data. The performance and efficiency results are presented in terms of percent root mean square difference (PRD). Finally, the new PRD technique has been proposed for performance measurement and compared with the existing PRD technique; which has shown that proposed new PRD technique achieved minimum value of PRD with improved results.
2017 International Conference on Robotics, Automation and Sciences (ICORAS), 2017
The proposed method is a contrast measurement technique which makes use of the second-order deriv... more The proposed method is a contrast measurement technique which makes use of the second-order derivatives of the pixels in MRI images. The technique utilizes the changes in the histogram of second-order derivatives for the image, specifically the region under a Laplacian curve which approximates the distribution of the pixel frequencies in the histogram. As gain is introduced, the region of the histogram under the curve changes. The standard deviation of the frequency differences between the histogram and the curve in the region is used as a measurement score. The objective of this method is to provide a way to include small details, such as small blood vessels, in the contrast measurement, compared to local average contrast and RMS contrast measurements which do not. The outcome is a scoring method which not only provides a measurement of contrast, but also indicates the loss of small details in MRI images.
Histopathological analysis of tissues has been gaining a lot of interests recently, from developi... more Histopathological analysis of tissues has been gaining a lot of interests recently, from developing computer algorithms to assist pathologists from cell detection and counting, to tissue classification and cancer grading. With the advent of whole slide imaging, the field of digital pathology has gained enormous popularity, and is currently regarded as one of the most promising avenues of diagnostic medicine. Deep learning advancement on image set today has successfully evolved as many models has been proposed and produced state-of-the-art object classifying results. This is not limited to large database such as Imagenet but also has seen applications in other medical image analysis related areas. In this paper we have carefully constructed and expanded the deep model network to classify normal and tumor slides in histology images of lymph nodes tissue. We have proposed our own deep learning model based on convolutional neural network with smaller requirement using 64×64×3 input imag...
Locally Linear Embedding (LLE) is an unsupervised non-linear manifold learning method, which has ... more Locally Linear Embedding (LLE) is an unsupervised non-linear manifold learning method, which has spurred increased interest in face recognition research recently. However, it is commonly known that a supervised method that considering the class-specific information always outperforms the unsupervised one, especially in biometric recognition task. In this paper, we propose a supervised LLE technique, known as class-label Locally Linear Embedding (cLLE). cLLE aims to discover the nonlinearity of high-dimensional data by minimizing the global reconstruction error of the set of all local neighbours in the data set. cLLE method is using user class-specific information in neighbourhoods selection and thus preserves the local neighbourhoods. Since the locality preservation is correlated to the class discrimination, the proposed cLLE is expected superior to LLE in face recognition. Experimental results on three face databases demonstrate the success of the proposed technique.
In sports science, two widely used approaches to perform movement recognition and analysis are th... more In sports science, two widely used approaches to perform movement recognition and analysis are through manual annotation of sports video and physical body marker attached to athlete’s body. The use of physical body markers, however, requires expertise on visual annotation which is obviously time-consuming and inconvenient for the athletes. Badminton is one of Malaysia’s most popular sports but there is still a lack of scientific research on movement recognition and analysis focusing on this sport. Therefore, in this paper, a novel lossless compact view invariant compression technique with a dynamic time warping algorithm is proposed to cater for both badminton movement recognition and analysis frameworks. Our experimental dataset of depth map sequences composed of 10 types of badminton movements with a total of 600 samples performed by 20 badminton players. The dataset varies in terms of viewpoints, human body size, clothes, speed, and gender. Experimental results revealed that near...
With the increasing prevalence rate of diabetes and obesity worldwide, chronic wounds are becomin... more With the increasing prevalence rate of diabetes and obesity worldwide, chronic wounds are becoming a significant burden for world health and economy. The treatment of a chronic wound goes through complex and time-intensive process. During the healing period, continuous wound measurement helps clinicians to predict the healing time and monitor the treatment efficiency. Current clinical techniques such as ruler-based or tracing-based methods are inaccurate, time-consuming and also subject to intra- and inter-reader variability that does not satisfy a comprehensive clinical benchmark. In this paper, we proposed a method for wound boundary demarcation and estimation based on Super pixel segmentation and classification using an enhanced convolution neural network. An overall accuracy, sensitivity, and specificity of around 90% was observed, which fared much better against traditional methods.
2018 IEEE Student Conference on Research and Development (SCOReD), 2018
Chronic wound is becoming a major threat for world health and economy. In the USA alone, an estim... more Chronic wound is becoming a major threat for world health and economy. In the USA alone, an estimated 6.5 million people are affected by the chronic wound and the annual cost for chronic wound treatment is reportedly more than 25 billion dollars. The process of chronic wound healing is very complex and time-consuming. Quantification of wound size plays a vital role for clinical wound treatment as the physical dimension of a wound is an important clue for wound assessment. The current techniques for wound area measurement are the ruler method and tracing which is mainly based on visual inspection, thus are not very accurate as well as time-consuming. A computerized wound measurement system can provide a more accurate measurement, reduce bias and errors due to fatigue and can potentially reduce clinical workload. In this paper, we proposed a simple but efficient method for wound area segmentation based on superpixel classification with color and texture feature and SVM classifier. Som...
Monitoring the changes in wound size over time is one of the most important parameters for assess... more Monitoring the changes in wound size over time is one of the most important parameters for assessment of the applied treatment plan in wound care. The current manual methods for wound measurement have several drawbacks including high error rates, time consuming and also causing patient discomfort because of pain or infection. There are some computerized tools available in clinical settings known as digital planimetry which require the clinician to identify wound boundaries and calibrate them manually. However these methods are a barrier to achieving clinical quality benchmarks as the accuracy decreases drastically with improper camera lens orientation and some other factors. This paper proposes an automated calibration method for wound measurement based on the segmentation of reference label card and geometrical image operations. Due to the uncontrolled setting of the camera for our images, segmentation of the label card can be very challenging, thus preventing accurate calibration and wound size measurement. We proposed a label card segmentation method based on probability map approach and superpixel region growing. Theoretically, the white label card regions would have a distinguished probability map in the white channel compared to other regions, generating a reliable initial segmentation. Morphological operations and edge detection are then performed to obtain a more accurate final segmentation. Based on the segmented label card, the size of the pixel can be calibrated, and the size of the wound can be automatically calculated. Experimental results confirm the feasibility of the proposed method by demonstrating segmentation accuracy in terms of the Jaccard index of 94.77%, Dice coefficient of 97.20%, and contour matching score of 94.21%. The wound measurement accuracy also shows very promising results.
2021 IEEE 17th International Colloquium on Signal Processing & Its Applications (CSPA)
The presence of robots in our daily life is becoming more common, where robots start carrying out... more The presence of robots in our daily life is becoming more common, where robots start carrying out more complex tasks. This increase in the complexity of tasks makes conventional control system insufficient. Therefore, a plausible approach is required for robots to learn how to perform these tasks. Reinforcement learning enables robots to perform complex tasks without highly engineered control systems. However, using reinforcement learning in robotic applications is challenged by several problems such as high dimensionality. Thus, in this paper, we study the performance of the Hindsight Experience Replay (HER) algorithm which addresses the high dimensionality problem. In this paper, we analyze the algorithm performance using a simulated robotic arm to pick and place different objects. Then, we propose the use of vision feedback which is used to control the gripper of the robotic arm. The results and analysis highlights some of HER limitations when dealing with objects that have limited grasping points. Our proposed method allows the robotic arm to pick objects using the same trained policy without the need to retrain the agent for new objects. Finally, we prove that using our method the robotic arm can pick the objects with higher success rate compared to the one without vision feedback.
An individual with profound deafness or total hearing loss has a hearing threshold of 80dB or mor... more An individual with profound deafness or total hearing loss has a hearing threshold of 80dB or more. The ineffectiveness of hearing aids, surging costs and complex surgeries for cochlear implants have discouraged many to opt for these types of treatments. Hence, this research aims to provide an alternative hearing aid that stimulates “hearing” through the skin sensory, which is more affordable and accessible for the profoundly deaf or total hearing loss community. We have developed four initial vibrating transducers with single spectrum, which are strapped to a belt. The transducers pick up audible sounds through a microphone, amplifies the sound to a high-level signal, stimulating a vibration pattern on the human skin sensory. The belt was tested on 30 random people who identified as normal, partial, and profoundly deaf. When the belt was strapped to the individual’s waist, audible sound was played (stimulus) and the individual was asked whether he/she can feel a stimulation or vi...
International Journal of Pattern Recognition and Artificial Intelligence
A tremendous increase in the video content uploaded on the internet has made it necessary for aut... more A tremendous increase in the video content uploaded on the internet has made it necessary for auto-recognition of videos in order to analyze, moderate or categorize certain content that can be accessed easily later on. Video analysis requires the study of proficient methodologies at the semantic level in order to address the issues such as occlusions, changes in illumination, noise, etc. This paper is aimed at the analysis of the soccer videos and semantic processing as an application in the video semantic analysis field. This study proposes a framework for automatically generating and annotating the highlights from a soccer video. The proposed framework identifies the interesting clips containing possible scenes of interest, such as goals, penalty kicks, etc. by parsing and processing the audio/video components. The framework analyzes, separates and annotates the individual scenes inside the video clips and saves using kernel support vector machine. The results show that semantic a...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2018
Hormone receptor status in breast carcinoma is determined primarily to identify patients who may ... more Hormone receptor status in breast carcinoma is determined primarily to identify patients who may benefit from hormonal therapy. Estrogen receptor (ER) is one of the hormone receptor positive factors which have been recognized as a marker for which women with breast cancer would respond to hormone treatment. We propose a system to classify cells in ER-stained whole slide breast carcinoma images according to their staining strength using convolutional neural network (CNN). The proposed CNN multiclass classifier was tested on a region of 1200 cells, and achieved very promising results, with overall accuracy of 88.8% and AUC score of 97.5%. The proposed system is useful for use in hormone receptor testing, where the outcomes are used to decide whether the cancer is likely to respond to hormonal therapy or other treatments.
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Papers by Fazly Abas