Active contours for intensity inhomogeneous image segmentation

La “inhomogeneidad” (falta d'homogeneïtat) d'intensitat és un problema ben conegut en la segmentació d'imatges, la qual cosa afecta la precisió dels mètodes de segmentació basats en la intensitat. En aquesta tesi, es proposen mètodes de contorn actiu basat en fronteres i regions per segmentar imatges inhomogènies. En primer lloc, s'ha proposat un mètode de contorn actiu basat en fronteres mitjançant Diferència de Gaussianes (DoG), que ajuda a segmentar l'estructura global de la imatge. En segon lloc, hem proposat un mètode de contorn actiu basat en regions per corregir i segmentar imatges inhomogènies. S'ha utilitzat un nucli de transformació de fase (phase stretch transform - PST) per calcular noves intensitats mitjanes i camps de polarització, que s'empren per definir una imatge ajustada de polarització. En tercer lloc, s'ha proposat un altre mètode de contorn actiu basat en regions utilitzant un funcional d'energia basat en imatges ajustades locals i globals. El camp de polarització s'aproxima amb una distribució Gaussiana i el biaix de les regions no homogènies es corregeix dividint la imatge original pel camp aproximat de polarització. Finalment, s'ha proposat un mètode híbrid de contorns actius multifàsic (quatre fases) per dividir una imatge de RM cerebral en tres regions diferents: matèria blanca (WM), matèria grisa (GM) i líquid cefaloraquidi (CSF). En aquest treball, també s'ha dissenyat un mètode de post-processat (correcció de píxels) per millorar la precisió de les regions WM, GM i CSF segmentades. S'han utilitzat resultats experimentals tant amb imatges sintètiques com amb imatges reals de RM del cervell per a una comparació quantitativa i qualitativa amb mètodes de contorns actius de l'estat de l'art per mostrar els avantatges de les tècniques de segmentació proposades.La “inhomogeneidad” (falta de homogeneidad) de intensidad es un problema bien conocido en la segmentación de imágenes, lo que afecta la precisión de los métodos de segmentación basados en la intensidad. En esta tesis, se proponen métodos de contorno activo basado en bordes y regiones para segmentar imágenes inhomogéneas. En primer lugar, se ha propuesto un método de contorno activo basado en fronteras mediante Diferencia de Gaussianas (DoG), que ayuda a segmentar la estructura global de la imagen. En segundo lugar, hemos propuesto un método de contorno activo basado en regiones para corregir y segmentar imágenes inhomogéneas. Se ha utilizado un núcleo de transformación de fase (phase stretch transform - PST) para calcular nuevas intensidades medias y campos de polarización, que se emplean para definir una imagen ajustada de polarización. En tercer lugar, se ha propuesto otro método de contorno activo basado en regiones utilizando un funcional de energía basado en imágenes ajustadas locales y globales. El campo de polarización se aproxima con una distribución Gaussiana y el sesgo de las regiones no homogéneas se corrige dividiendo la imagen original por el campo aproximado de polarización. Finalmente, se ha propuesto un método híbrido de contornos activos multifásico (cuatro fases) para dividir una imagen de RM cerebral en tres regiones distintas: materia blanca (WM), materia gris (GM) y líquido cefalorraquídeo (CSF). En este trabajo, también se ha diseñado un método de post-procesado (corrección de píxeles) para mejorar la precisión de las regiones WM, GM y CSF segmentadas. Se han utilizado resultados experimentales tanto con imágenes sintéticas como con imágenes reales de RM del cerebro para una comparación cuantitativa y cualitativa con métodos de contornos activos del estado del arte para mostrar las ventajas de las técnicas de segmentación propuestas.Intensity inhomogeneity is a well-known problem in image segmentation, which affects the accuracy of intensity-based segmentation methods. In this thesis, edge-based and region-based active contour methods are proposed to segment intensity inhomogeneous images. Firstly, we have proposed an edge-based active contour method based on the Difference of Gaussians (DoG), which helps to segment the global structure of the image. Secondly, we have proposed a region-based active contour method to both correct and segment intensity inhomogeneous images. A phase stretch transform (PST) kernel has been used to compute new intensity means and bias field, which are employed to define a bias fitted image. Thirdly, another region-based active contour method has been proposed using an energy functional based on local and global fitted images. Bias field is approximated with a Gaussian distribution and the bias of intensity inhomogeneous regions is corrected by dividing the original image by the approximated bias field. Finally, a hybrid region-based multiphase (four-phase) active contours method has been proposed to partition a brain MR image into three distinct regions: white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). In this work, a post-processing (pixel correction) method has also been devised to improve the accuracy of the segmented WM, GM and CSF regions. Experimental results with both synthetic and real brain MR images have been used for a quantitative and qualitative comparison with state-of-the-art active contour methods to show the advantages of the proposed segmentation techniques

An advanced level set method based on Bregman divergence for inhomogeneous image segmentation

© 2017 IEEE. Intensity inhomogeneity often occurs in real images. Local information based level set methods are comparatively effective in segmenting image with inhomogeneous intensity. However, in practice, these models suffer from local minima and high computational cost. In this paper, a novel region-based level set method based on Bregman divergence and local binary fitting, hereafter referred to as Bregman-LBF, is proposed for image segmentation. The proposed method utilizes global and local information to formulate a new energy function. The Bregman-LBF model enjoys the following advantages: (1) Bregman-LBF outperforms the piece-wise constant(PC) model in handling intensity inhomogeneity. (2) Bregman-LBF is more effective than the local binary fitting (LBF) model and more robust than the global and local intensity fitting (GLIF) model. The relationship between the Bregman-LBF model and the existing models, e.g. the Chan-Vese(CV) model, is discussed. The experiments conducted on synthetic and benchmark image datasets have shown that the proposed Bregman-LBF outperforms the piece-wise constant (PC) model in handling intensity inhomogeneity. The experimental results have also shown that the Bregman-LBF is more effective than the local binary fitting (LBF) model and more robust than the global and local intensity fitting (GLIF) model

Dual-channel active contour model for megakaryocytic cell segmentation in bone marrow trephine histology images

Assessment of morphological features of megakaryocytes (special kind of cells) in bone marrow trephine biopsies play an important role in the classification of different subtypes of Philadelphia-chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs). In order to aid hematopathologists in the study of megakaryocytes, we propose a novel framework that can efficiently delineate the nuclei and cytoplasm of these cells in digitized images of bone marrow trephine biopsies. The framework first employs a supervised machine learning approach that utilizes color and texture features to delineate megakaryocytic nuclei. It then employs a novel dual-channel active contour model to delineate the boundary of megakaryocytic cytoplasm by using different deconvolved stain channels. Compared to other recent models, the proposed framework achieves accurate results for both megakaryocytic nuclear and cytoplasmic delineation

Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours

6Segmentation accuracy is an important criterion for evaluating the performance of segmentation techniques used to extract objects of interest from images, such as the active contour model. However, segmentation accuracy can be affected by image artifacts such as intensity inhomogeneity, which makes it difficult to extract objects with inhomogeneous intensities. To address this issue, this paper proposes a hybrid region-based active contour model for the segmentation of inhomogeneous images. The proposed hybrid energy functional combines local and global intensity functions; an incorporated weight function is parameterized based on local image contrast. The inclusion of this weight function smoothens the contours at different intensity level boundaries, thereby yielding improved segmentation. The weight function suppresses false contour evolution and also regularizes object boundaries. Compared with other state-of-the-art methods, the proposed approach achieves superior results over synthetic and real images. Based on a quantitative analysis over the mini-MIAS and PH2 databases, the superiority of the proposed model in terms of segmentation accuracy, as compared with the ground truths, was confirmed. Furthermore, when using the proposed model, the processing time for image segmentation is lower than those when using other methods.openopenMemon A.A.; Soomro S.; Shahid M.T.; Munir A.; Niaz A.; Choi K.N.Memon, A. A.; Soomro, S.; Shahid, M. T.; Munir, A.; Niaz, A.; Choi, K. N