Amar Partap Singh Pharwaha

ANALYSIS AND DESIGN OF CIRCULAR FRACTAL ANTENNA USING ARTIFICIAL NEURAL NETWORKS

A Neural Network is a simplifled mathematical model based on Biological Neural Network, which can be considered as an extension of conventional data processing technique. In this paper, an Artiflcial Neural Network (ANN) based simple approach is proposed as forward side for the design of a Circular Fractal Antenna (CFA) and analysis as reverse side of problem. Proposed antenna is simulated up to 2nd iteration using method of moment based IE3D software. Antenna is fabricated on Roger RT 5880 Duroid substrate (High frequency material) for validation of simulated, measured and ANN results. The main advantage of using ANN is that a properly trained neural network completely bypasses the complex iterative process for the design and analysis of this antenna. Results obtained by using artiflcial neural networks are in accordance with the simulated and measured results.

AUTOMATIC DETECTION OF MICROCALCIFICATIONS IN DIGITIZED MAMMOGRAMS USING FUZZY 2-PARTITION ENTROPY AND MATHEMATICAL MORPHOLOGY

Cancer is a leading cause of death among men and women nowadays all over the world. Breast cancer is a most common form of cancer originated from breast tissue among women. Most frequent type of breast cancer is ductal carcinoma in situ (DCIS) and most frequent symptoms of DCIS recognized by mammography are clusters of Microcalcifications (MCCs). Automatic detection of Microcalcifications is an important task to prevent and treat the disease. In this paper, an effective approach for automatic detection of Microcalcifications in digitized mammograms is proposed. The proposed approach is based on fuzzy 2-partition entropy and mathematical morphology. In the proposed approach, first phase uses fuzzy Gaussian membership function for mammogram fuzzification. In this phase, fuzzy 2-partition entropy approach is used to find bandwidth of the Gaussian function. After this, mathematical morphological enhancement approach is used to enhance the contrast of Microcalcifications in mammograms. Finally, Microcalcifications are located using Otsu threshold selection method. Experiments have been conducted on images of mini-MIAS database (Mammogram Image Analysis Society database (UK)). In order to validate the results, several different kinds of standard test images (fatty, fatty-glandular and denseglandular) of mini-MIAS database are considered. Experimental results demonstrate that the proposed approach has an ability to detect Microcalcifications even in dense mammograms. The results of proposed approach are quite promising. The proposed approach can be a part of developing a computer aided decision (CAD) system for early detection of breast cancer.

Image Segmentation Using Two-Dimensional Renyi Entropy

Segmentation of an image is used to separate the image into several significant parts based on properties of discontinuity and similarity. Segmentation of an image is generally done with the help of thresholding technique. Thresholding is used to turn an image from gray scale to binary. The selection of suitable threshold value in the image is a challenging task. Thresholding value depends upon the randomness of intensity distribution of the image. Entropy is a parameter that is used to measure the randomness of intensity distribution of the image. In this work, Shannon-entropy-based and Non-Shannon (Renyi, Collision and Min) entropy-based approaches are used to select suitable threshold value. After this, thresholding values obtained from different approaches are tested on 6 standard test images. For evaluating, peak signal-to-noise ratio (PSNR) and uniformity (U) parameters are used. From the results, it is observed that Renyi-entropy-based approach is a better approach than other approaches.

DIGITAL MAMMOGRAM ENHANCEMENT USING KAPUR MEASURE OF ENTROPY AND MATHEMATICAL MORPHOLOGY

Mammography is the most reliable, effective, low cost and highly sensitive method for early detection of breast cancer. Mammogram analysis usually refers to the processing of mammograms with the goal of finding abnormality presented in the mammogram. Mammogram enhancement is one of the most critical tasks in automatic mammogram image analysis. Main purpose of mammogram enhancement is to enhance the contrast of details and subtle features while suppressing the background heavily. In this paper, a hybrid approach is proposed to enhance the contrast of microcalcifications while suppressing the background heavily, using fuzzy logic and mathematical morphology. First, mammogram is fuzzified using Gaussian fuzzy membership function whose bandwidth is computed using Kapur measure of entropy. After this, mathematical morphology is applied on fuzzified mammogram. Mathematical morphology provides tools for the extraction of microcalcifications even if the microcalcifications are located on a nonuniform background. ...