A review on the current segmentation algorithms for medical images

This paper makes a review on the current segmentation algorithms used for medical images. Algorithms are divided into three categories according to their main ideas: the ones based on threshold, the ones based on pattern recognition techniques and the ones based on deformable models. The main tendency of each category with their principle ideas, application field, advantages and disadvantages are discussed. For each considered type some typical algorithms are described. Algorithms of the third category are mainly focused because of the intensive investigation on deformable models in the recent years. Possible applications of these algorithms on segmenting organs and tissues contained in the pelvic cavity are also discussed through several preliminary experiments

Una metodología basada en espiral aplicada al análisis de células en una imagen

The advances in technology, microscopy and computing have allowed the development of new fields in cell image analysis. However, the usability of these platforms is adequate to expert users only. Many software tools are oriented to expert users in image processing, likewise the use of bioinformatics require a basic knowledge in programming. The development of research in cell imaging requires the joint work of computer Scientifics and biologist. In this paper we present a methodology to develop a software solution applied to the analysis of cell images.Los avances en tecnología, microscopía y computación han permitido el desarrollo de nuevos campos en el análisis de imágenes celulares. Sin embargo, la usabilidad de estas plataformas es adecuada solo para usuarios expertos. Muchas herramientas software están orientadas a usuarios expertos en el procesamiento de imágenes y así mismo el uso de herramientas bioinformáticas requiere un conocimiento básico en programación. El desarrollo de investigaciones en imágenes celulares requiere el trabajo conjunto de biólogos y de expertos en computación. En este artículo se presenta una metodología para desarrollar una solución de software aplicada al análisis de imágenes celulares

3D vasculature segmentation using localized hybrid level-set method

Background: Intensity inhomogeneity occurs in many medical images, especially in vessel images. Overcoming the difficulty due to image inhomogeneity is crucial for the segmentation of vessel image. Methods: This paper proposes a localized hybrid level-set method for the segmentation of 3D vessel image. The proposed method integrates both local region information and boundary information for vessel segmentation, which is essential for the accurate extraction of tiny vessel structures. The local intensity information is firstly embedded into a region-based contour model, and then incorporated into the level-set formulation of the geodesic active contour model. Compared with the preset global threshold based method, the use of automatically calculated local thresholds enables the extraction of the local image information, which is essential for the segmentation of vessel images. Results: Experiments carried out on the segmentation of 3D vessel images demonstrate the strengths of using locally specified dynamic thresholds in our level-set method. Furthermore, both qualitative comparison and quantitative validations have been performed to evaluate the effectiveness of our proposed model. Conclusions: Experimental results and validations demonstrate that our proposed model can achieve more promising segmentation results than the original hybrid method does

3D segmentation of glioma from brain MR images using seeded region growing and fuzzy c-means clustering

This thesis presents two algorithms for brain MR image segmentation. The images used are axial MR images of the human brain. The images show a glioma. The objective is to segment the tumour and edema surrounding it from the images. Initially the images are pre-processed by contrast adjustment. Segmentation is performed by two algorithms: seeded region growing and fuzzy c-means clustering. After the images are segmented, the volumes of the segmented regions are measured. The segmentation is done in MATLAB. Finally the results are rendered in 3D in AMIRA

Regional Segmentation Methods for Objects of Interest Identification from Medical Images – a Laboratory Task

Vizuální obrazová informace hraje důležitou roli téměř ve všech oblastech našeho života. V dnešní době je většina těchto informací reprezentována v digitální formě obrazu. Toto zobrazování je všudy přítomné od televizního obrazu, přes digitální fotografie, až po snímky CT. Tato práce se bude zabývat předzpracováním a následnou segmentací medicínských obrazů. Medicínské obrazy budou obsahovat záznamy z oblasti CT a MRI. Cílem této práce je aplikace předzpracování obrazu pomocí mediánové filtrace a následné použití metod Otsu prahování a K-means shlukovaní. Dosažené výsledky se použijí na zhodnocení výkonosti jednotlivých metod. Výkonost metod se bude hodnotit pomocí evaluačních parametrů. Použity budou parametry MSE, PSNR a index korelace. Celá práce je řešena v prostředí MATLAB. Z výsledné analýzy se získají důležité informace o vlastnostech vybraných metod. Práce bude zakončená návrhem vlastní laboratorní úlohy.Visual imaging information plays a fundamental role in almost every aspect of our lives. Nowadays, most of this information presents itself the digital form of an image. This kind of imagery is ubiquitous, from a television picture, digital photographs to a CT image. The thesis deals with pretreatment and the following segmentation of medical images. Medical images include footage from CT and MRI. The aim of the thesis is to evaluate the pretreatment image application using the median filter technique, successive by the usage of Otsu thresholding and K-means clustering methods. Attained results will be used for an efficient evaluation of each method used. The efficiency of the methods will be evaluated via evaluation parameters. MSE, PSNR, and a correlation coefficient will be used as the parameters for the evaluation. The entire thesis makes use of the MATLAB software. The final analysis processes the important information relating to the features of the method used. The thesis ends with the proposition of an educational laboratory task.450 - Katedra kybernetiky a biomedicínského inženýrstvívelmi dobř