<p>Image Segmentation plays a very important role in image processing. The single-mindedness of image segmentation is to partition the image into a set of disconnected regions with the homogeneous and uniform attributes like intensity, tone, color and texture. There are various methods for image segmentation but no method is suitable for low contrast images. In this paper, we are presenting an efficient and optimal thresholding image segmentation technique that can be used to separate the object and background pixels of the image to improve the quality of low contrast images. This innovative method consists of two steps. Firstly fuzzy logics are used to find optimum mean value using S-curve with automatic selection of controlled parameters to avoid the fuzziness in the image. Secondly, the fuzzy logic’s optimal threshold value used in Otsu method to improve the contrast of the image. This method, gives better results than traditional Otsu and Fuzzy logic techniques. The graphs and tables of values show that the proposed method is superior to traditional methods.</p>

The innovative approach consists of using the same algorithmic core for processing images to detect both microcalcifications and masses. Despite the advancement in the medical sciences cancer is claiming more than 50% of the people afflicted by it every year. Of all cancer incidence women around the world, the most commonly diagnosed type of non-skin cancer which results in death is Breast Cancer and this can be best detected by digital mammography. This paper includes the design and development of software expert system for real time mammogram image analysis. The system so designed would give the radiologist an idea about the exact shape and size of any tumor present in the breast. Radiologists however are unable to detect the cancerous growth when benign though it is detected in the mammograms due to varying criteria like dense flesh around the cancer or distractions due to neighboring features. This problem will be resolved by using Digital Image Processing techniques like Image Segmentation where the image will be segmented into similar regions by meaningfully assigning class labels to similar pixels in a region. Hence the cancerous growth will be detected in its early stage and the Radiologists will be able to do better diagnosis because Image Segmentation techniques are simple yet very effective. In this paper an innovative method is applied which consist mainly three steps. In the first step normalizes the regions in the breast images through uniform distribution of histogram equalization. In the second step fuzzy logic is applied to remove ambiguity in the misclassification region and in the third step a new Weight is applied to the previously extended OTSU method.

Image Thresholding is a necessary task in many image processing applications. In this paper we derive fuzzy rules for π-function. We use π-function to fuzzify the original image; this is constructed to locate the intensities of the misclassification regions. Based on information theory, it maximizes the information between image foreground and background. The merit of using fuzzy set is its ability to handle uncertainty and its robustness. This technique is to optimize the image threshold by effective selection of Region Of Interest (ROI). In general Valley seeking approaches are utilized to select a threshold if the histogram is bimodal. However, histograms would not be bimodal. The fuzzy region range of the π-function is chosen as one standard deviation of the arithmetic mean ). Because, the fuzzy region is spread on both sides of the image mean and the non-fuzzy data is located outside of this region. The limitation with the parent version is semi supervised, for low contrast images human perception is required. There exists no unsupervised appropriate procedure in literature to address this problem. The proposed method successfully segments the images of bimodal and multi-model histograms. The experimental results confirm the superiority of the proposed method over existing methods in performance. Our method produces more accurate and reliable results compared to the parent algorithm. This claim has been verified with some experimental trials using all categories of real world images.

No abstract

   In this epoch Medical Image segmentation is one of the most challenging problems in the research field of MRI scan image classification and analysis. The importance of image segmentation is to identify various features of the image that are used for analyzing, interpreting and understanding of images. Image segmentation for MRI of brain is highly essential due to accurate detection of brain tumor. This paper presents an efficient image segmentation technique that can be used for detection of tumor in the Brain. This innovative method consists of three steps. First is Image enhancement to improve the quality of the tumor image by eliminating noise and to normalize the image. Second is fuzzy logic which produce optimal threshold to avoid the fuzziness in the image and makes good regions regarding Image and tumor part of the Image. Third is novel OTSU technique applied for separating the tumor regions in the MRI. This method has produced better results than traditional extended OTSU method.