Fuzzy Enhancement Technique Research Articles

An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

Breast cancer is the most common type of cancer, and it is the reason for cancer death toll in women in recent years. Early diagnosis is essential to handle breast cancer patients for treatment at the right time. Screening with mammography is the preferred examination for breast cancer, as it is available worldwide and inexpensive. Computer-Aided Detection (CAD) systems are used to analyze medical images to detect breast cancer, early. The death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after operations. Processing of mammogram images has four main steps: pre-processing, segmentation of the region of interest, feature extraction and classification of the images into normal or abnormal classes. This paper presents an efficient framework for processing of mammogram images and introduces an algorithm for segmentation of the images to detect masses. The pre-processing step of mammogram images includes removal of digitization noise using a 2D median filter, removal of artifacts using morphological operations, and contrast enhancement using a fuzzy enhancement technique. The proposed fuzzy image enhancement technique is analyzed and compared with conventional techniques based on an Enhancement Measure (EME) and local contrast metrics. The comparison shows an outstanding performance of the proposed technique from the visual and numerical perspectives. The segmentation process is performed using Otsu's multiple thresholding method. This method segments the image regions into five classes with variable intensities using four thresholds. Its effectiveness is measured based on visual quality of the segmentation output, as it gives details about the image and positions of masses. The performance of the proposed framework is measured using Dice coefficient, Hausdorff, and Peak Signal-to-Noise Ratio (PSNR) metrics. The segmented tumor region with the proposed segmentation method is 81% of the ground truth region provided by an expert. Hence, the proposed framework achieves promising results for aiding radiologists in screening of mammograms, accurately.

Design and Analysis Of Underwater Imaging Application susing High Frequency Array

Underwater picture preparing has a few applications in the field of maritime exploration work and logical applications, for example, archaeology, geography, underwater ecological appraisal, laying of significant distance gas pipelines and correspondence joins across the mainlands which request geo-referential looking over of the maritime bed and prospection of complex task. The lowering of a camera underwater requires satisfactory lodging utilizing high frequency. The moving of the camera with the assistance from distant spot or face to face at the site is similarly a perplexing undertaking. In any case, the significant test is forced by underwater medium properties. Underwater dimness picture upgrade has acquired far and wide significance with the quick advancement of present day imaging gear. Notwithstanding, the difference upgrade of single underwater dim picture is a difficult errand for logical investigation and computational applications. Versatile differentiation upgrade calculations to determine the picture fluffiness was proposed to suit underwater pictures with shifting difference. Fuzzy edge held intensification strategy had the option to furnish better upgrade with very much protected edge data and improved differentiation in examination with the fuzzy enhancement technique. Acoustic imaging systems are likewise valuable for reviewing or inspecting objects when water turbidity blocks the utilization of closed circuit television or other optical methods for survey. Likewise, light is weakened with a horizontal distance of proliferation, decreasing the light energy arriving at the camera, accordingly bringing about a deficiency of the normal tone.

Improvement of Fuzzy Image Contrast Enhancement Using Simulated Ergodic Fuzzy Markov Chains

This paper presents a novel fuzzy enhancement technique using simulated ergodic fuzzy Markov chains for low contrast brain magnetic resonance imaging (MRI). The fuzzy image contrast enhancement is proposed by weighted fuzzy expected value. The membership values are then modified to enhance the image using ergodic fuzzy Markov chains. The qualitative performance of the proposed method is compared to another method in which ergodic fuzzy Markov chains are not considered. The proposed method produces better quality image.