Mammogram Image Segmentation

Mammography is a popular technique but it has its limitations especially in younger women and in denser breasts. The Computer-Aided-Diagnosis has been proposed for the medical prognosis [7-9]. The fuzzy logic and Artificial Neural Network form the basis of the intelligent systems. There are several instances where the artificial intelligence is used for the diagnosis of the breast cancer. The methods have included many Artificial Neural Networks architectures such as Convolution Neural Network [10], Radial Basis Network [11], General Regression Neural Network [11], Probabilistic Neural Network [11], Resilient Back propagation Neural Network [12], and hybrid with Fuzzy Logic [13]. In this paper [7] a supervised artificial neural network [14-16] was used to help classify the breast lesions into malignant and benign classes by processing the computer cytology images. The accuracy of the trained neural network was found to be 82.21%. The ANN has been established as a robust system for the diagnosis of breast cancer [18].There is a complex relationship between different biomarkers which were identified for the diagnosis of this cancer [19], the MLP neural network was simulated for the diagnosis using four biomarkers

Image segmentation is a very important and challenging task that is needed for the purpose of diagnosing brain tumors and other neurological diseases. Brain tumors have different characteristics such as size, shape, location, and image intensities. They may deform neighbouring structures and if there is edema with the tumor, intensity properties of the nearby region change [1]. In adults, the most common and cancer-causing tumor type is glial tumors that have a high mortality rate. They occur in the glial cells of the brain and show a rapid growth by extending into the healthy brain tissues. Image segmentation is the separation of an image into segments called classes or subsets, according to one or more characteristics or features, and enhancing

Medical image processing is the demanding task and emerging field. Medical imaging procedure will view the images that are present in internal portions of the human body for medical analysis. Automatic Brain tumor segmentation is a perceptive stage in medical field. In the MR images, the tumor part can be seen clearly by the accurate size and the correct measurement for the treatment. A necrotic part can be differentiated from the surrounding tissue. The automatic image segmentation algorithm, which exploit the information obtain from deducting tumor in brain MRI. Segmentation algorithm that will segment brain MR images into tumor, white matter, gray matter and cerebrospinal fluid separately. In this paper, improved automatic image segmentation

There are numerous reasons why a woman’s breast tissue can be negatively affected over time. However, this does not necessarily mean you are out for the count. You can avoid all the hassles and instead depend on medical procedures to overcome this problem once and for all. There are two types of breast augmentation procedures to choose from, and in this article, we will take a closer look at both so you can make the right decision:

The cost of breast augmentation can vary depending on whether you have saline or silicone implants. There are some important questions to ask, as you should not base your decision about where to have surgery only on price.

Patients in Breast augmentation recovery are usually asked to limit physical exertion for 3 to 7 days before gradually getting back to more rigorous activities. The amount of rest needed and the amount of time required for a total recovery where you are able to get back to regular activities will be based on the locations of the incisions, the implant size as well as if the breast implant is located over or under the pectoral muscle.

Breast cancers are classified by several grading systems. Histopathology grading depends on the histological appearance of tumor. Grade of tumor compares the appearance of the breast cancer cells to the appearance of normal breast tissue with low grade being as well and high grade as poorly differentiated .Classification of Malignant Tumors (TNM) uses three

Being a leader is not a title, it is about having a clear vision, power to challenge and capability to influence.

By looking at a sample of breast cancer tissue under a microscope the cancer can be graded:

the medical experts to understand the severity of the disease and the nature of the

Abstract: Incidence rate of skin cancer are increasing day by day. Skin cancer is one of the deadliest forms of cancer but detected earlier can save the life time of the human being. An automated screening system is introduced to identify the presence of skin cancer in advance. In this paper, texture distinctiveness lesion segmentation algorithm is used. Experience and training-based characteristics of back propagation neural network is used with texture distinctiveness lesion segmentation algorithm, for identifying the normal and abnormal portions of skin .The most commonly occurring skin cancers are Melanoma, Basal and squamous cell carcinoma and actinic keratosis. The proposed system is to diagnose the presence of these skin cancers with high segmentation accuracy.

The diagnosis of the Breast Cancer is done by classification of the tumor. The tumor is of two types benign or malignant. The malignant tumor is cancer. Unfortunately, it is difficult to predict the type of the tumors for the physicians. An appropriate and reliable detection system is therefore needed to detect the malignant tumor. There is

Abstract – Ultrasound imaging is one of the popular, cheapest and noninvasive medical scans. At the time of image acquisition, there may be distortion of the quality of image in the form of speckle noise. Nowadays, many researches have made various experiments to enhance the quality of medical image. However, there is scope to further enhance the image. In the proposed method, finding out the seed pixel randomly is the basic problem, which is treated as an optimization problem. It can be solved by Particle Swarm Optimization. Using Particle Swarm Optimization algorithm, the fitness function can give us appropriate seed pixel for required ultrasound imaging. In this paper, a novel method is proposed, wherein segmentation will be applied on a fuzzy filtered image. The fuzzy filter applies fuzzy rules to detect regions in the image viz. edge region, homogenous region, and noisy region by using different gradients, and later filters the noisy region using fuzzy membership rules. The proposed method has been tested on different ultrasound images, and the experimental results demonstrate its effectiveness.

The proposed model is a fuzzy rough hybrid system for diagnosing breast cancer patients. The diagnoses system is composed of preprocessing and classification phases. The hybrid is consisted of three main sub modules. The first sub module is responsible for the selection process. It preprocesses the data sets by eliminating the irrelative attributes. The framework utilizes a fuzzy rough algorithm to handle the uncertainty nature of the medical data. The second sub module produces an intelligent classifier of the diseases. It uses the rough neural network intelligence to learn from the uncertain reduced data set. After training, the rough neural network becomes the intelligent classifier of the unseen cases to predict their medical condition of the illness. The third sub module measures the accuracy and time complexities of the intelligent classifier by the test data set. Figure 5 shows the main sub modules and their

Abstract—The input CT image is processed out by Single Image Saliency. A Single image saliency is obtained by measuring the contrast and spatial cue of a CT image. The kernel for single image saliency is obtained by fusing contrast and spatial cue. This kernel matrix is convolved with the CT image in the saliency filter, there by improvising the image quality which is further used for tumor detection. The image obtained from the saliency filter is segmented into squared sub- regions from which mean and standard deviation are estimated from where the liver region is obtained. The same holds good for the detection of tumor region as well. The liver region is further processed by adaptive thresholding to identify the liver region, and this region is segmented by morphological operations and Region growing method. And the tumor region is finally segmented out by edge based active contour model using distance regularized level set evolution.