With the growth of image processing applications, image segmentation has become an important part of image processing. The simplest method to segment an image is thresholding. Using the thresholding method, segmentation of an image is done by fixing all pixels whose intensity values are more than the threshold to a foreground value. The remaining pixels are set to a background value. S uch technique can be used to obtain binary images from grayscale images. The conventional thresholding techniques use a global threshold for all pixels, whereas adaptive thresholding changes the threshold value dynamically over the image. This paper offers a comparative study on adaptive thresholding techniques to choose the accurate method for binarizing an image based on the contrast, texture, resolution etc. of an image.

According to the recent surveys, breast cancer has become one of the major causes of mortality rate among women. Breast cancer can be defined as a group of rapidly growing cells that lead to the formation of a lump or an extra mass in the breast tissue which consequently leads to the formation of tumor. Tumors can be classified as malignant (cancerous) or benign (non-cancerous). Feature selection is an important parameter in determining the classification systems. Machine learning methods are the most commonly used methods among researchers for breast cancer diagnosis. This paper proposes to investigate the WBCD (Wisconsin Breast Cancer Dataset) which comprises of 683 patients and implements the chosen features to train the back propagation neural network. The performance is then analyzed on the basis of classification accuracy, sensitivity, specificity, positive and negative predictor values, receiver operating characteristic curves and confusion matrix. A total of 9 features has been used to classify breast cancer with an accuracy of 99.27%. According to the recent surveys, breast cancer has become one of the major causes of mortality rate among women. Breast cancer can be defined as a group of rapidly growing cells that lead to the formation of a lump or an extra mass in the breast tissue which consequently leads to the formation of tumor. Tumors can be classified as malignant (cancerous) or benign (non-cancerous). Feature selection is an important parameter in determining the classification systems. Machine learning methods are the most commonly used methods among researchers for breast cancer diagnosis. This paper proposes to investigate the WBCD (Wisconsin Breast Cancer Dataset) which comprises of 683 patients and implements the chosen features to train the back propagation neural network. The performance is then analyzed on the basis of classification accuracy, sensitivity, specificity, positive and negative predictor values, receiver operating characteristic curves and confusion matrix. A total of 9 features has been used to classify breast cancer with an accuracy of 99.27%.

MEDLINE is the largest biomedical literature database. It is updated daily with 200-4,000 citations. This permanent growth induces the need of a good MEDLINE abstract clustering to accelerate the procedure of research and information retrieval. Several works have been developed in this context, but clustering MEDLINE abstracts are still an area where researchers are trying to propose new approaches to better clustering. Over the last few years, evolutionary algorithms have been widely applied to clustering problems because of their ability to avoid local optimal solutions and converge to a global one. In this article, a new approach is proposed for clustering MEDLINE abstracts based on an extension of an evolutionary algorithm which is the genetic algorithm combined with a Vector Space Model and an agglomerative algorithm.

In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.

In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.