Abstract-The primary purpose of this paper is to elaborate upon and to take a step ahead on the research done in the field of Image Processing with a focus on Early Tumor Detection through the use of Magnetic Resonance Imaging (MRI) and Image Processing Tool Box of MATLAB. The technique which the author proposed is Morphological Reconstruction Based Segmentation, used to segment the solid cum cystic tumor of brain and is suggested after testing and observing various available methods and algorithms. The proposed method shows more precision amongst others and the processing time is also fast.
Abstract. Medical imaging makes use of the technology to noninvasively reveal the internal structure of the human body. By using medical imaging modalities patient's life can be improved through a precise and rapid treatment without any side effects. The main purpose of this paper is to develop an automatic method that can accurately classify a tumor from abnormal tissues. The images used for tumor segmentation have been obtained from MRI modality. In this research we have developed a novel image segmentation technique based on catchments basins and ridge lines to accurately segment the brain image. This technique is based on immersions techniques for extracting objects from the image based on internal markers and external markers. Direct application of watershed transform leads to over-segmentation due to the presence of noise and other irregularities inherent in digital images. So to avoid this, we have carried out some preprocessing to remove noise and attenuate the curvilinear structures present in the MRI images during acquisition stage. After preprocessing step we calculated the morphological gradient of the input images. Then both internal and external markers of the original images were calculated and finally the watershed transform applied to complete the segmentation process. We have tested our algorithms on images obtained from Brain Atlas data base and found that the results closely match that of the radiologist. intuitive idea of watershed transform comes from the geography where mountains correspond to high gray levels whereas rivers and valleys corresponds to low intensity values [6]. Watershed Transform is a segmentation method which is based on regions with homogenous properties of intensities [7]. Direct application of WT segmentation method to the MR images produces over-segmentation due to the presence of noise and other local irregularities of the images. Over-segmentation can cause serious problem to the MRI images which render the result of algorithm practically ineffective. This over-segmentation can lead to large number of segmented regions, which may not be the objective of the research. In order to avoid this problem of oversegmentation a practical solution is to limit the allowable regions by incorporating some preprocessing techniques. This preprocessing stage will remove the noise and local minima's. The practical approach to avoid over-segmentation is the use of markers for foreground objects and background. A marker is a connected component belonging to an image. So for segmentation purpose we have two types of markers. One is called as internal markers related to the object of interest and external markers related with the background. There are many methods in the literature for marker selection. People have used different methods ranging from gray level values and connectivity, descriptions involving size, shape, location, relative distance measure and texture based markers selection. The advantage of using markers is that it brings a priori knowledge to help efficient segmentat...
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