Combination of polar edge detection and active contour model for automated tongue segmentation

Author name used in this publication: David ZhangBiometrics Research Centre, Department of ComputingVersion of RecordPublishe

Endocardial Border Detection Using Radial Search and Domain Knowledge

The ejection fraction rate is a frequently used parameter when treating patients who suffered from heart disease. However, the measurement of this ejection rate depends on manual segmentation of left ventricle cavity in the end-systolic and end-diastolic phases. This paper proposes a semi-automatic algorithm for the detection of left ventricular border in two dimensional long axis ultrasound echocardiographic images. First, we apply a preprocessing filter to the ultrasound for the sake of speckle reduction. Then the knowledge of the anatomical structure of human heart and local homogeneity of blood pool is being used to detect the border of left ventricle. The proposed method evaluates 80 ultrasound images from four healthy volunteers and the generated contours are compared with contours manually drawn by an expert. The measured Dice Metric and Hausdorff Distance recorded by the proposed algorithm are 85.1% ± 0.4% and 3.25 ± 0.46 mm respectively. The numerical results reported in this paper indicate that the proposed algorithm is able to correctly segment the left ventricle cavity and can be used as an alternative to manual contouring of left ventricle cavity from ultrasound images

Localizing Region-Based Level-set Contouring for Common Carotid Artery in Ultrasonography

 This work developed a fully-automated and efficient method for detecting contour of common carotid artery in the cross section view of two-dimensional B-mode sonography. First, we applied a preprocessing filter to the ultrasound image for the sake of reducing speckle. An adaptive initial contouring method was then performed to obtain the initial contour for level set segmentation. Finally, the localizing region-based level set segmentation automatically extracted the precise contours of common carotid artery. The proposed method evaluated 130 ultrasound images from three healthy volunteers and the segmentation results were compared to the boundaries outlined by an expert. Preliminary results showed that the method described here could identify the contour of common carotid artery with satisfactory accuracy in this dataset

Leukaemia’s Cells Pattern Tracking Via Multi-phases Edge Detection Techniques

Edge detection involves identifying and tracing the sudden sharp discontinuities to extract meaningful information from an image. The purpose of this paper is to improve detecting the leukaemia edges in the blood cell image. Toward this end, two distinctive procedures are developed which are Ant Colony Optimization Algorithm and the gradient edge detectors (Sobel, Prewitt and Robert). The latter involves image filtering, binarization, kernel convolution filtering and image transformation. Meanwhile, ACO involves filtering, enhancement, detection and localisation of the edges. Finally, the performance of the edge detection methods ACO, Sobel, Prewitt and Robert is compared to determine the best edge detection method. The results revealed that the Prewitt edge detection method produced an optimal performance for detecting edges of leukaemia cells with a value of 107%. Meanwhile, the ACO, Sobel and Robert yielded performance results of 76%, 102% and 93% respectively. Overall findings indicated that the gradient edge detection methods are superior to the Ant Colony Optimization method

Intra- and inter-observer variability in the angiographic delineation of brain arterio-venous malformations (AVMs)

Colloque avec actes et comité de lecture. internationale.International audienceThe purpose of this study is to determine the intra- and inter-observer variability in the manual delineation of the boundaries of brain arterio-venous malformations (AVMs) on digital subtracted angiograms. Such delineation is used to define the target volume in stereotactic radiotherapy

Semiautomated segmentation of bone marrow biopsies images based on texture features and Generalized Regression Neural Networks

This work presents preliminary results of a method for semi-automatic detection of fat and hematopoietic cells as well as trabecular surfaces in bone marrow biopsies, in order to calculate the percentage of each type of tissue or cell area in relation to the whole area. Experimental results using selected clinical cases are presented. Twenty six biopsies were used, presenting varied distributions of cellularity and trabeculae topography. The approach is based on Digital Image Processing techniques and a Neural Network used for classification using textural features obtained from biopsies images. Results were improved with Mathematical Morphology filters. The algorithm produces highly satisfactory results. The method was shown to be faster and more reproducible than conventional ones, like region growing, edge detection, split and merging. The results from this computer-assisted technique are compared to others obtained by visual inspection by two expert pathologists, and differences of less than 9 % are observed.Eje: II - Workshop de computación gráfica, imágenes y visualizaciónRed de Universidades con Carreras en Informática (RedUNCI

Advanced deep learning methodology for accurate, real-time segmentation of high-resolution intravascular ultrasound images

AIMS: The aim of this study is to develop and validate a deep learning (DL) methodology capable of automated and accurate segmentation of intravascular ultrasound (IVUS) image sequences in real-time. METHODS AND RESULTS: IVUS segmentation was performed by two experts who manually annotated the external elastic membrane (EEM) and lumen borders in the end-diastolic frames of 197 IVUS sequences portraying the native coronary arteries of 65 patients. The IVUS sequences of 177 randomly-selected vessels were used to train and optimise a novel DL model for the segmentation of IVUS images. Validation of the developed methodology was performed in 20 vessels using the estimations of two expert analysts as the reference standard. The mean difference for the EEM, lumen and plaque area between the DL-methodology and the analysts was ≤0.23mm2 (standard deviation ≤0.85mm2), while the Hausdorff and mean distance differences for the EEM and lumen borders was ≤0.19 mm (standard deviation≤0.17 mm). The agreement between DL and experts was similar to experts' agreement (Williams Index ranges: 0.754-1.061) with similar results in frames portraying calcific plaques or side branches. CONCLUSIONS: The developed DL-methodology appears accurate and capable of segmenting high-resolution real-world IVUS datasets. These features are expected to facilitate its broad adoption and enhance the applications of IVUS in clinical practice and research

ANALYSIS OF GALL-BLADDER IMAGES BY USING STATIONARY WAVELET TRANSFORM AND DISCRETE WAVELET TRANSFORM

Ayrıt sezimi algoritmaları biyomedikal görüntü analizinde önemli algoritmalardır. Bu çalışmada ayrıt sezimi için, histogram eşleme, ayrık dalgacık dönüşümü (ADD) ve durağan dalgacık dönüşümü (DDD) yöntemleri, safra kesesi ses üstü imgelerinin kalitesini iyileştirmede kullanılmıştır. Ayrıca, ortanca süzgeçleme algoritması, bu tekniklerden sonra görüntü üzerine uygulanmıştır. Sonuçta bu algoritmaların başarımı, görüntü entropi, parçalı t-testi ve CPU zamanı gibi çeşitli başarım ölçütleri kulllanılarak karşılaştırılmıştır. The edge detection algorithms are important in biomedical image analysis. In this work histogram equalization, the discrete wavelet transform and the stationary wavelet transform techniques were used to improve the quality of the gall bladder ultrasonic images for edge detection. Also the median filtering algorithm was used after applying the both techniques. Then the performances of these algorithms were compared by several performance measures such as image entropy, paired t-test, and CPU time