Our contribution presents a research progress in our long-term project that deals with spine anal... more Our contribution presents a research progress in our long-term project that deals with spine analysis in computed tomography (CT) data. A fully automatic computer-aided diagnosis (CAD) system is presented, enabling the simultaneous segmentation and classification of metastatic tissues that can occur in the vertebrae of oncological patients. The task of the proposed CAD system is to segment metastatic lesions and classify them into two categories: osteolytic and osteoblastic. These lesions, especially osteolytic, are ill defined and it is difficult to detect them directly with only information about voxel intensity. The use of several local texture and shape features turned out to be useful for correct classification, however the exact determination of relevant image features is a difficult task. For this reason, the feature determination has been solved by automatic feature extraction provided by a deep convolutional neural network (CNN). The achieved mean sensitivity of detected lesions is greater than 92% with approximately three false positive detections per lesion for both types.
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015
Two novel statistically based methods for bone lesion detection and classification are presented.... more Two novel statistically based methods for bone lesion detection and classification are presented. Together with the previously published MRF method [15], they form a triad of mutually complementary methods that promise, when fused, to enable higher reliability of bone lesion assessment.
Digital Signal Filtering, Analysis and Restoration
Let us repeat that we understand a continuous, one-dimensional signal to be a piecewise continuou... more Let us repeat that we understand a continuous, one-dimensional signal to be a piecewise continuous real or complex function of one continuous real variable, /. Usually the dimensionality will not be emphasised as, with the exception of Chapter 14, we will deal exclusively with one-dimensional signals. Note that the number of independent variables is not, in principle, limited; we can have two, three or multidimensional signals. Signals that are continuous (according to our definition) are also called analogue signals as they can be represented by time courses of physical ('analogue') variables (t then specifically means time).
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019
The optimal rotational alignment of brain Computed Tomography (CT) images to a required standard ... more The optimal rotational alignment of brain Computed Tomography (CT) images to a required standard position has a crucial importance for both automatic and manual diagnostic analysis. In this contribution, we present a novel two-step iterative approach for the automatic 3D rotational alignment of brain CT data. The angles of axial and coronal rotations are determined by an unsupervised by localisation of the Midsagittal Plane (MSP) method. This includes detection and pairing of medially symmetrical feature points. The sagittal rotation angle is subsequently estimated by regression convolutional neural network (CNN). The proposed methodology has been evaluated on a dataset of CT data manually aligned by radiologists. It has been shown that the algorithm achieved the low error of estimated rotations (≈1 degree) and in a significantly shorter time than the experts (≈2 minutes per case).
Computer Methods and Programs in Biomedicine, 2019
BACKGROUND AND OBJECTIVE We present a fully automatic system based on learning approaches, which ... more BACKGROUND AND OBJECTIVE We present a fully automatic system based on learning approaches, which aims to localization and identification (labeling) of vertebrae in 3D computed tomography (CT) scans of possibly incomplete spines in patients with bone metastases and vertebral compressions. METHODS The framework combines a set of 3D algorithms for i) spine detection using a convolution neural network (CNN) ii) spinal cord tracking based on combination of a CNN and a novel growing sphere method with a population optimization, iii) intervertebral discs localization using a novel approach of spatially variant filtering of intensity profiles and iv) vertebra labeling using a CNN-based classification combined with global dynamic optimization. RESULTS The proposed algorithm has been validated in testing databases, including also a publicly available dataset. The mean error of intervertebral discs localization is 4.4 mm, and for vertebra labeling, the average rate of correctly identified vertebrae is 87.1%, which can be considered a good result with respect to the large share of highly distorted spines and incomplete spine scans. CONCLUSIONS The proposed framework, which combines several advanced methods including also three CNNs, works fully automatically even with incomplete spine scans and with distorted pathological cases. The achieved results allow including the presented algorithms as the first phase to the fully automated computer-aided diagnosis (CAD) system for automatic spine-bone lesion analysis in oncological patients.
It is essential that differently oriented specialists and students involved in image processing h... more It is essential that differently oriented specialists and students involved in image processing have a firm grasp of the necessary concepts and principles. A single-source reference that can provide this foundation, as well as a thorough explanation of the techniques involved, ...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2007
This paper presents a method for geometrical and time-delay auto-calibration of an ultrasonic com... more This paper presents a method for geometrical and time-delay auto-calibration of an ultrasonic computed tomography (USCT) system. The algorithms used for the calibration are based on the principles similar to the global positioning system (GPS) navigation. Ultrasonic transmitters and receivers in USCT can be viewed like satellite transmitters and mobile receiver units in GPS. However, unlike in GPS, none of the positions of the transmitters or receivers in USCT are assumed to be known and all are the to-be-calibrated unknowns. The presented method is capable of calibrating the positions of all ultrasonic transducers and their individual time delays at once. No calibration phantoms are necessary.
2009 9th International Conference on Information Technology and Applications in Biomedicine, 2009
The contribution aims at designing and testing an automatic method to estimate the status of the ... more The contribution aims at designing and testing an automatic method to estimate the status of the retinal neural fibre layer (NFL) based on analysing the output of the most common ophthalmological imaging modality - fundus camera images. As the neural layer manifests itself in these images rather faintly and is often hardly visible, the method has to utilise subtle features
In this work, we propose a new approach for three-dimensional registration of MR fractional aniso... more In this work, we propose a new approach for three-dimensional registration of MR fractional anisotropy images with T1-weighted anatomy images of human brain. From the clinical point of view, this accurate coregistration allows precise detection of nerve fibers that is essential in neuroscience. A template matching algorithm combined with normalized cross-correlation was used for this registration task. To show the suitability of the proposed method, it was compared with the normalized mutual information-based B-spline registration provided by the Elastix software library, considered a reference method. We also propose a general framework for the evaluation of robustness and reliability of both registration methods. Both registration methods were tested by four evaluation criteria on a dataset consisting of 74 healthy subjects. The template matching algorithm has shown more reliable results than the reference method in registration of the MR fractional anisotropy and T1 anatomy image data. Significant differences were observed in the regions splenium of corpus callosum and genu of corpus callosum, considered very important areas of brain connectivity. We demonstrate that, in this registration task, the currently used mutual information-based parametric registration can be replaced by more accurate local template matching utilizing the normalized cross-correlation similarity measure.
IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2012
The paper is focused on sound-speed image reconstruction in 3-D ultrasound transmission tomograph... more The paper is focused on sound-speed image reconstruction in 3-D ultrasound transmission tomography. Along with ultrasound reflectivity and the attenuation coefficient, sound speed is an important parameter which is related to the type and pathological state of the imaged tissue. This is important in the intended application, breast cancer diagnosis. In contrast to 2-D ultrasound transmission tomography systems, a 3-D system can provide an isotropic spatial resolution in the x-, y-, and z-directions in reconstructed 3-D images of ultrasound parameters. Several challenges must, however, be addressed for 3-D systems-namely, a sparse transducer distribution, low signal-to-noise ratio, and higher computational complexity. These issues are addressed in terms of sound-speed image reconstruction, using edge-preserving regularized algebraic reconstruction in combination with synthetic aperture focusing. The critical points of the implementation are also discussed, because they are crucial to enable a complete 3-D image reconstruction. The methods were tested on a synthetic data set and on data sets measured with the Karlsruhe 3-D ultrasound computer tomography (USCT) I prototype using phantoms. The sound-speed estimates in the reconstructed volumes agreed with the reference values. The breast-phantom outlines and the lesion-mimicking objects were also detectable in the resulting sound-speed volumes.
The paper presents an overview of image analysis activities of the Brno DAR group in the medical ... more The paper presents an overview of image analysis activities of the Brno DAR group in the medical application area of retinal imaging. Particularly, illumination correction and SNR enhancement by registered averaging as preprocessing steps are briefly described; further mono- and multimodal registration methods developed for specific types of oph-thalmological images, and methods for segmentation of optical disc, retinal vessel tree and autofluorescence areas are presented. Finally, the designed methods for neural fibre layer detection and evaluation on retinal images, utilising different combined texture analysis ap-proaches and several types of classifiers, are shown. The results in all the areas are shortly commented on at the respective sections. In order to emphasise methodological aspects, the methods and results are ordered according to consequential phases of processing rather then divided according to individual medical applications.
Our contribution presents a research progress in our long-term project that deals with spine anal... more Our contribution presents a research progress in our long-term project that deals with spine analysis in computed tomography (CT) data. A fully automatic computer-aided diagnosis (CAD) system is presented, enabling the simultaneous segmentation and classification of metastatic tissues that can occur in the vertebrae of oncological patients. The task of the proposed CAD system is to segment metastatic lesions and classify them into two categories: osteolytic and osteoblastic. These lesions, especially osteolytic, are ill defined and it is difficult to detect them directly with only information about voxel intensity. The use of several local texture and shape features turned out to be useful for correct classification, however the exact determination of relevant image features is a difficult task. For this reason, the feature determination has been solved by automatic feature extraction provided by a deep convolutional neural network (CNN). The achieved mean sensitivity of detected lesions is greater than 92% with approximately three false positive detections per lesion for both types.
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015
Two novel statistically based methods for bone lesion detection and classification are presented.... more Two novel statistically based methods for bone lesion detection and classification are presented. Together with the previously published MRF method [15], they form a triad of mutually complementary methods that promise, when fused, to enable higher reliability of bone lesion assessment.
Digital Signal Filtering, Analysis and Restoration
Let us repeat that we understand a continuous, one-dimensional signal to be a piecewise continuou... more Let us repeat that we understand a continuous, one-dimensional signal to be a piecewise continuous real or complex function of one continuous real variable, /. Usually the dimensionality will not be emphasised as, with the exception of Chapter 14, we will deal exclusively with one-dimensional signals. Note that the number of independent variables is not, in principle, limited; we can have two, three or multidimensional signals. Signals that are continuous (according to our definition) are also called analogue signals as they can be represented by time courses of physical ('analogue') variables (t then specifically means time).
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019
The optimal rotational alignment of brain Computed Tomography (CT) images to a required standard ... more The optimal rotational alignment of brain Computed Tomography (CT) images to a required standard position has a crucial importance for both automatic and manual diagnostic analysis. In this contribution, we present a novel two-step iterative approach for the automatic 3D rotational alignment of brain CT data. The angles of axial and coronal rotations are determined by an unsupervised by localisation of the Midsagittal Plane (MSP) method. This includes detection and pairing of medially symmetrical feature points. The sagittal rotation angle is subsequently estimated by regression convolutional neural network (CNN). The proposed methodology has been evaluated on a dataset of CT data manually aligned by radiologists. It has been shown that the algorithm achieved the low error of estimated rotations (≈1 degree) and in a significantly shorter time than the experts (≈2 minutes per case).
Computer Methods and Programs in Biomedicine, 2019
BACKGROUND AND OBJECTIVE We present a fully automatic system based on learning approaches, which ... more BACKGROUND AND OBJECTIVE We present a fully automatic system based on learning approaches, which aims to localization and identification (labeling) of vertebrae in 3D computed tomography (CT) scans of possibly incomplete spines in patients with bone metastases and vertebral compressions. METHODS The framework combines a set of 3D algorithms for i) spine detection using a convolution neural network (CNN) ii) spinal cord tracking based on combination of a CNN and a novel growing sphere method with a population optimization, iii) intervertebral discs localization using a novel approach of spatially variant filtering of intensity profiles and iv) vertebra labeling using a CNN-based classification combined with global dynamic optimization. RESULTS The proposed algorithm has been validated in testing databases, including also a publicly available dataset. The mean error of intervertebral discs localization is 4.4 mm, and for vertebra labeling, the average rate of correctly identified vertebrae is 87.1%, which can be considered a good result with respect to the large share of highly distorted spines and incomplete spine scans. CONCLUSIONS The proposed framework, which combines several advanced methods including also three CNNs, works fully automatically even with incomplete spine scans and with distorted pathological cases. The achieved results allow including the presented algorithms as the first phase to the fully automated computer-aided diagnosis (CAD) system for automatic spine-bone lesion analysis in oncological patients.
It is essential that differently oriented specialists and students involved in image processing h... more It is essential that differently oriented specialists and students involved in image processing have a firm grasp of the necessary concepts and principles. A single-source reference that can provide this foundation, as well as a thorough explanation of the techniques involved, ...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2007
This paper presents a method for geometrical and time-delay auto-calibration of an ultrasonic com... more This paper presents a method for geometrical and time-delay auto-calibration of an ultrasonic computed tomography (USCT) system. The algorithms used for the calibration are based on the principles similar to the global positioning system (GPS) navigation. Ultrasonic transmitters and receivers in USCT can be viewed like satellite transmitters and mobile receiver units in GPS. However, unlike in GPS, none of the positions of the transmitters or receivers in USCT are assumed to be known and all are the to-be-calibrated unknowns. The presented method is capable of calibrating the positions of all ultrasonic transducers and their individual time delays at once. No calibration phantoms are necessary.
2009 9th International Conference on Information Technology and Applications in Biomedicine, 2009
The contribution aims at designing and testing an automatic method to estimate the status of the ... more The contribution aims at designing and testing an automatic method to estimate the status of the retinal neural fibre layer (NFL) based on analysing the output of the most common ophthalmological imaging modality - fundus camera images. As the neural layer manifests itself in these images rather faintly and is often hardly visible, the method has to utilise subtle features
In this work, we propose a new approach for three-dimensional registration of MR fractional aniso... more In this work, we propose a new approach for three-dimensional registration of MR fractional anisotropy images with T1-weighted anatomy images of human brain. From the clinical point of view, this accurate coregistration allows precise detection of nerve fibers that is essential in neuroscience. A template matching algorithm combined with normalized cross-correlation was used for this registration task. To show the suitability of the proposed method, it was compared with the normalized mutual information-based B-spline registration provided by the Elastix software library, considered a reference method. We also propose a general framework for the evaluation of robustness and reliability of both registration methods. Both registration methods were tested by four evaluation criteria on a dataset consisting of 74 healthy subjects. The template matching algorithm has shown more reliable results than the reference method in registration of the MR fractional anisotropy and T1 anatomy image data. Significant differences were observed in the regions splenium of corpus callosum and genu of corpus callosum, considered very important areas of brain connectivity. We demonstrate that, in this registration task, the currently used mutual information-based parametric registration can be replaced by more accurate local template matching utilizing the normalized cross-correlation similarity measure.
IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2012
The paper is focused on sound-speed image reconstruction in 3-D ultrasound transmission tomograph... more The paper is focused on sound-speed image reconstruction in 3-D ultrasound transmission tomography. Along with ultrasound reflectivity and the attenuation coefficient, sound speed is an important parameter which is related to the type and pathological state of the imaged tissue. This is important in the intended application, breast cancer diagnosis. In contrast to 2-D ultrasound transmission tomography systems, a 3-D system can provide an isotropic spatial resolution in the x-, y-, and z-directions in reconstructed 3-D images of ultrasound parameters. Several challenges must, however, be addressed for 3-D systems-namely, a sparse transducer distribution, low signal-to-noise ratio, and higher computational complexity. These issues are addressed in terms of sound-speed image reconstruction, using edge-preserving regularized algebraic reconstruction in combination with synthetic aperture focusing. The critical points of the implementation are also discussed, because they are crucial to enable a complete 3-D image reconstruction. The methods were tested on a synthetic data set and on data sets measured with the Karlsruhe 3-D ultrasound computer tomography (USCT) I prototype using phantoms. The sound-speed estimates in the reconstructed volumes agreed with the reference values. The breast-phantom outlines and the lesion-mimicking objects were also detectable in the resulting sound-speed volumes.
The paper presents an overview of image analysis activities of the Brno DAR group in the medical ... more The paper presents an overview of image analysis activities of the Brno DAR group in the medical application area of retinal imaging. Particularly, illumination correction and SNR enhancement by registered averaging as preprocessing steps are briefly described; further mono- and multimodal registration methods developed for specific types of oph-thalmological images, and methods for segmentation of optical disc, retinal vessel tree and autofluorescence areas are presented. Finally, the designed methods for neural fibre layer detection and evaluation on retinal images, utilising different combined texture analysis ap-proaches and several types of classifiers, are shown. The results in all the areas are shortly commented on at the respective sections. In order to emphasise methodological aspects, the methods and results are ordered according to consequential phases of processing rather then divided according to individual medical applications.
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Papers by Jiri Jan