Brain tumor, a mass of tissue that grows out of control is one of the major causes for the increase in mortality among children and adults. Segmenting the regions of brain is the major challenge in tumor detection. A large number of effective segmentation algorithms have been used for segmentation in grey scale images ranging from simple edge-based methods to composite high-level approaches using modern and advanced pattern recognition approaches. Gradient vector field is an effective methodology applied to extract objects from complex backgrounds. The methodology has been effectively applied to extract different types of cancer like breast, skin, stomach etc. This paper uses a segmentation methodology called Gradient Vector Field, which uses energy as the feature to segment brain tumor along with a number of standard object detection algorithms mainly Sobel, Canny, Roberts, Prewitt and Laplacian. The performance of all the algorithms is tested on synthetic datasets followed by real MRI images. This paper (i) concludes the superiority of a particular methodology over others (ii) explains in detail the runtime analysis of the algorithms (iii) In depth analysis of the manual calculations of the parameters related to all the algorithms resulting into an optimized result with minimum error.

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Abstract. Automated learning systems used to extract information from images play a major role in document analysis. Optical character recognition or OCR has been widely used to automatically segment and index the documents from a wide space. Most of the methods used for OCR recognition and extraction like HMM's, Neural etc, mentioned in literature have errors which require human operators to be rectified and fail to extract images with blur as well as illumination variance. This paper explains proposes an enhancement supported threshold based pre-processing methodology for word spotting in Marathi printed bimodal images using image segmentation. The methodology makes use of an enhanced image obtained by histogram equalization followed by followed by age segmentation using a specific threshold. The threshold can be obtained using genetic algorithms. GA based segmentation technique is codified as an optimization problem used efficiently to search maxima and minima from the histogram of the image to obtain the threshold for segmentation. The system described is capable of extracting normal as well as blurred images and images for different lighting conditions. The same inputs are tested for a standard GA based methodology and the results are compared with the proposed method. The paper further elaborates the limitations of the method.

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