local thresholding
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<p>Detection of continuous and connected edges is very important in many applications, such as detecting oil slicks in remote sensing and detecting cancers in medical images. The detection of such edges is a hard problem particularly in noisy images and most edge detection algorithms suffer from producing broken and thick edges in such images. The main goal of this thesis is to reduce broken edges by proposing an optimisation model and a solution method in order to detect edges in noisy images. This thesis suggests a newapproach in the framework of particle swarm optimisation (PSO) to overcome noise and reduce broken edges through exploring a large area and extracting the global structure of the edges. A fitness function is developed based on the possibility score of a curve being fitted on an edge and the curvature cost of the curve with two constraints. Unlike traditional algorithms, the new method can detect edges with greater continuity in noisy images. Furthermore, a new truncation method within PSO is proposed to truncate the real values of particle positions to integers in order to increase the diversity of the particles. This thesis also proposes a local thresholding technique for the PSObased edge detection algorithm to overcome the problem of detection of edges in noisy images with illuminated areas. The local thresholding technique is proposed based on themain idea of the Sauvola-Pietkinenmethod which is a way of binarisation of illuminated images. It is observed that the new local thresholding can improve the performance of the PSO-based edge detectors in the illuminated noisy images.  Since the performance of using static topologies in various applications and in various versions of PSO is different , the performance of six different static topologies (fully connected, ring, star, tree-based, von Neumann and toroidal topologies)within threewell-known versions of PSO (Canonical PSO, Bare Bones PSO and Fully Informed PSO) are also investigated in the PSO-based edge detector. It is found that different topologies have different effects on the accuracy of the PSO-based edge detector. This thesis also proposes a novel dynamic topology called spatial random meaningful topology (SRMT) which is an adoptation version of a gradually increasing directed neighbourhood (GIDN). The new dynamic topology uses spatial meaningful information to compute the neighbourhood probability of each particle to be a neighbour of other particles. It uses this probability to randomly select the neighbours of each particle at each iteration of PSO. The results show that the performance of the proposed method is higher than that of other topologies in noisy images in terms of the localisation accuracy of edge detection.</p>

<p>Detection of continuous and connected edges is very important in many applications, such as detecting oil slicks in remote sensing and detecting cancers in medical images. The detection of such edges is a hard problem particularly in noisy images and most edge detection algorithms suffer from producing broken and thick edges in such images. The main goal of this thesis is to reduce broken edges by proposing an optimisation model and a solution method in order to detect edges in noisy images. This thesis suggests a newapproach in the framework of particle swarm optimisation (PSO) to overcome noise and reduce broken edges through exploring a large area and extracting the global structure of the edges. A fitness function is developed based on the possibility score of a curve being fitted on an edge and the curvature cost of the curve with two constraints. Unlike traditional algorithms, the new method can detect edges with greater continuity in noisy images. Furthermore, a new truncation method within PSO is proposed to truncate the real values of particle positions to integers in order to increase the diversity of the particles. This thesis also proposes a local thresholding technique for the PSObased edge detection algorithm to overcome the problem of detection of edges in noisy images with illuminated areas. The local thresholding technique is proposed based on themain idea of the Sauvola-Pietkinenmethod which is a way of binarisation of illuminated images. It is observed that the new local thresholding can improve the performance of the PSO-based edge detectors in the illuminated noisy images.  Since the performance of using static topologies in various applications and in various versions of PSO is different , the performance of six different static topologies (fully connected, ring, star, tree-based, von Neumann and toroidal topologies)within threewell-known versions of PSO (Canonical PSO, Bare Bones PSO and Fully Informed PSO) are also investigated in the PSO-based edge detector. It is found that different topologies have different effects on the accuracy of the PSO-based edge detector. This thesis also proposes a novel dynamic topology called spatial random meaningful topology (SRMT) which is an adoptation version of a gradually increasing directed neighbourhood (GIDN). The new dynamic topology uses spatial meaningful information to compute the neighbourhood probability of each particle to be a neighbour of other particles. It uses this probability to randomly select the neighbours of each particle at each iteration of PSO. The results show that the performance of the proposed method is higher than that of other topologies in noisy images in terms of the localisation accuracy of edge detection.</p>

Abstract Choroidal vascularity index (CVI) is a new biomarker defined for retinal optical coherence tomography (OCT) images for measuring and evaluating the choroidal vascular structure. CVI is the ratio of the choroidal luminal area (LA) to the total choroidal area (TCA). The automatic calculation of this index is important for ophthalmologists but has not yet been explored. In this study, we proposed a fully automated method based on deep learning for calculating CVI in three main steps: 1- segmentation of the choroidal boundary, 2- detection of the choroidal luminal vessels, and 3- computation of the CVI. The proposed method is evaluated in complex situations like the presence of diabetic retinopathy and pachychoroid spectrum. In pachychoroid spectrum, the choroid is thickened, and the boundary between choroid and sclera (sclerochoroidal junction) is blurred, which makes the segmentation more challenging. The proposed method is designed based on the U-Net model, and a new loss function is proposed to overcome the segmentation problems. The vascular LA is then calculated using Niblack’s local thresholding method, and the CVI value is finally computed. The experimental results for the segmentation stage with the best-performing model and the proposed loss function were used showed dice coefficients of 0.941 and 0.936 in diabetic retinopathy and pachychoroid spectrum patients, respectively. The unsigned boundary localization errors in the presence of diabetic retinopathy were 0.0020 and 0.0138 pixels for the BM boundary and sclerochoroidal junction, respectively. Similarly, the unsigned errors in the presence of pachychoroid spectrum were 0.0072 and 0.0254 pixels for BM and sclerochoroidal junction. The performance of the proposed method for calculating CVI was evaluated; the Bland-Altman plot indicated acceptable agreement between the values allocated by experts and the proposed method in the presence of diabetic retinopathy and pachychoroid spectrum.

<p>Segmentasi wajah merupakan bagian penting dalam pengolahan citra digital untuk mengetahui objek wajah dalam citra sebelum dilakukan pendeteksian ekspresi wajah. Adaptif <em>Threshold – Integral Image</em> adalah salah satu teknik segmentasi berbasis <em>pixel-based</em>,<em> </em>yaitu <em>local thresholding</em>. Penelitian ini bertujuan untuk memisahkan objek wajah manusia dan <em>background </em>-nya. Citra wajah yang akan digunakan nanti citra di dalam ruangan (<em>indoor</em>)<em> </em>dan di luar ruangan (<em>outdoor</em>) dengan resolusi gambar 300x400 piksel. Pada penelitian ini juga mencari nilai parameter S (<em>kernel</em>) dan T (<em>threshold</em>) yang terbaik dengan melakukan 16 kali percobaan. Dan didapatkan hasil terbaik, yaitu citra di dalam ruangan (<em>indoor</em>) nilai S=1/2 dan T=50, serta citra di luar ruangan (<em>outdoor</em>) nilai S=1/30 dan T=30. Segmentasi citra wajah dengan menggunakan metode Adaptif <em>Threshold – Integral Image</em> <em>robust</em> (kuat) terhadap intensitas cahaya tinggi dan rendah dengan mengatur nilai parameter S (<em>kernel</em>) dan T (<em>Threshold</em>) maka metode ini mampu memisahkan objek wajah dan <em>background</em> -nya. Dari hasil uji coba <em>threshold</em> menggunakan metode Adaptif <em>Threshold – Integral Image</em> terhadap citra di dalam ruangan (<em>indoor)</em> dan di luar ruangan (<em>outdoor)</em> menghasilkan <em>thresholding</em> yang baik dengan mempertimbangkan nilai parameter S (<em>kernel</em>) dan T (<em>threshold</em>) memberikan hasil dengan tingkat akurasi yang tinggi, yaitu citra di dalam ruangan (<em>indoor</em>) sebesar 96.72%, dan citra di luar ruangan (<em>outdoor</em>) sebesar 93.59%.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em>Face segmentation is an important in digital image processing to find out the object's face in the image before detecting facial expressions. Adaptive Threshold - Integral Image is a pixel-based segmentation technique, which is local thresholding. This study is intended to split the object of a human face and its background. Face images that will be used later in indoor and outdoor with an image resolution of 300x400 pixels. This study also searched for the best S (kernel) and T (threshold) parameter values by performing 16 experiments. And the best results are obtained, name the image in the room (indoor) the value of S = 1/2 and T = 50, and the image outside the room (outdoor) the value of S = 1/30 and T = 30. Face image segmentation using the Adaptive Threshold - Integral Image robust method of high and low light intensity by setting the S (kernel) and T (Threshold) parameter values, this method is able to split the face object and its background. From the results of the threshold trial using the Adaptive Threshold - Integral Image method for indoor and outdoor images produces a good thresholding by considering the values of the S (kernel) and T (threshold) parameters to give results with a high degree of accuracy, that is indoor images of 96.72%, and outdoor images of 93.59%.<strong></strong></em></p><p><em><strong><br /></strong></em></p>

AbstractSegmentation of scanning electron microscopy (SEM) images of focused ion beam (FIB) cross-sections through indented regions in nanoporous gold (np-Au) is carried out. A key challenge for image analysis of open porous materials is the appropriate binarization of the pore and gold ligament regions while excluding material lying below the cross-sectional plane. Here, a manual approach to thresholding is compared to global and local approaches. The global thresholding resulted in excessive deviations from the nominal solid fraction, due to a strong gray-scale gradient caused by the tilt angle during imaging and material shadowing. In contrast, the local thresholding approach delivered local solid fractions that were free of global gradients, and delivered a quality comparable to the manual segmentation. The extracted densification profiles vertically below the indenter as well as parallel to the surface showed an exponential-type decay from the indenter tip towards the nominal value of 1 far from the indenter. Graphic abstract

Segmentation of µm-resolution image data of irregularly shaped objects poses challenges to existing segmentation algorithms. This is especially true, when imperfections like noise, uneven lightning or traces of sample preparation are present in the image data. In this paper, considering electron micrographs of femoral quadriceps nerve sections of mice, a segmentation method to extract single axons surrounded by myelin sheaths is developed which is able to cope with various imperfections and artefacts. This approach successfully combines established methods like local thresholding and marker-based watershed transform to achieve a reliable segmentation of the given data. Indeed, the resulting segmentation map can be used to quantitatively determine geometrical characteristics of the axons and myelin sheaths. This is exemplified by modelling the joint probability distribution of axon area and myelin sphericity using a parametric copula approach, and by analysing the evolution of the model parameters for image data obtained from mice of different ages.

The eukaryotic cytoskeleton plays essential roles in cell signaling, trafficking, and motion. Recent work towards defining the temporal and spatial dynamics of cytoskeletal organization, including as a function of cell status, has utilized quantitative analysis of cytoskeletal fluorescence images as a standard approach to define cytoskeletal function. However, due to the uneven spatial distribution of the cytoskeleton, including varied shape and unstable binding efficiency to staining markers, these approaches may not segment cytoskeletal fractions accurately. Additionally, quantitative approaches currently suffer from human bias as well as information loss caused by z-axis projection of raw images. To overcome these obstacles, we developed Implicit Laplacian of Enhanced Edge (ILEE), a cytoskeletal component segmentation algorithm, which uses an 2D/3D-compatible, unguided local thresholding approach, therefore providing less biased and stable results. Empowered by ILEE, we constructed a Python based library for automated quantitative analysis of cytoskeleton images, which computes cytoskeletal indices that covers density, bundling, severing, branching, and directionality. Comparing to various classic approaches, ILEE library generates descriptive data with higher accuracy, robustness, and efficiency. In addition to the analysis described herein, we have developed an open-access ILEE library for community use.

The main aim of the Computer-Aided Detection/Diagnosis system is to assist the radiologists in examining the digital mammograms. Digital mammogram is the most popular screening technique for early detection of breast cancer. One of the problems in breast mammogram analysis is the presence of pectoral muscles region with high intensity in the upper right or left side of most Media-Lateral Oblique views of mammogram images. Therefore, it is important to remove this pectoral muscle from the image for accurate diagnosis results. The proposed method consists of three main steps. In the first step, noise is reduced using Median filtering. In the second step, artifacts removal and breast region extraction are performed using Otsu method. Finally, the pectoral muscle is extracted and removed using the proposed Split Orientation Local Thresholding (SOLTH) algorithm. For this study, a total of 110 mammogram images from the Mini-Mias database (MIAS) were used to evaluate the proposed method’s performance. The experimental results of automatic pectoral muscle detection and removal were observed by radiologist and showed 90.9% accuracy of acceptable results.

Binarization plays a crucial role in Optical Character Recognition (OCR) ancillary domains, such as recovery of degraded document images. In Document Image Analysis (DIA), selecting threshold is not trivial since it differs from one problem (dataset) to another. Instead of trying several different thresholds for one dataset to another, we consider noise inherency of document images in our proposed binarization scheme. The proposed stochastic architecture implements the local thresholding technique: Niblack’s binarization algorithm. We introduce a stochastic comparator circuit that works on unipolar stochastic numbers. Unlike the conventional stochastic circuit, it is simple and easy to deploy. We implemented it on the Xilinx Virtex6 XC6VLX760-2FF1760 FPGA platform and received encouraging experimental results. The complete set of results are available upon request. Besides, compared to conventional designs, the proposed stochastic implementation is better in terms of time complexity as well as fault-tolerant capacity.