Browsing by Author "Khan, Siddiqa bee Shamsher shenaaz (12CO80)"

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    Image segmentation for MRI brain tumor
    (AIKTC, 2015-05) Khan, Mubashir; Mapari, Waqas Moinuddin Zaibunnisa (12CO103); Khan, Siddiqa bee Shamsher shenaaz (12CO80); Rajwadkar, Ifrah Ayaz Zaibunnisa (11CO06)
    The task of detecting the position of tumor (the so called gross tu- mor volume-GT)in the body of the patient is the starting point for a medical treatment. In the conformal radiotherapy the tumor cells are irradiated and killed with high precision without damaging the neighboring healthy tissues. Usually, a medical doctor recognizes the GTV and designs its border lines manually on the computer tomography slices (CT-SCAN).The CT-SCAN procedure affects the consequence planning target volume (PTV) of irradiation, ei- ther if it is decided by the same medical doctor or by the automatic supporting system. The main objective of this study is to design a computer system able to detect the presence of a tumor in the digital images of the brain and to accurately define is border line. The basic assumption is that different local textures in images can describe different physical characteristics corresponding to differ- ent objects. We will be using this in wide range of field such as Bio medical informatics, Oceanography, Computer vision. We assume that the local texture of tumor cells is highly different from local texture of other biological tissues. Thus texture measurement in the image could be part of an effective discrimination technique between healthy tissues and possible tumor areas. A computer system has been design to recognize the typical fea- 1 tures of the tumor from the digital images of the brain. The basic concept is that local texture in the images can reveal the typical regularities of the biological structures. Thus, the textual features have been extracted using a co-occurrence matrix approach. The level of recognition, among three possible types of image areas are: tumor, non tumor, non tumor and back ground. We are focus- ing on tumor image segmentation .