Automated detection of breast cancer using SAXS data and wavelet features

The overarching goal of this project was to improve breast cancer screening protocols first by collecting small angle x-ray scattering (SAXS) images from breast biopsy tissue, and second, by applying pattern recognition techniques as a semi-automatic screen. Wavelet based features were generated from the SAXS image data. The features were supplied to a classifier, which sorted the images into distinct groups, such as “normal” and “tumor”. The main problem in the project was to find a set of features that provided sufficient separation for classification into groups of “normal” and “tumor.” In the original SAXS patterns, information useful for classification was obscured. The wavelet maps allowed new scale-based information to be uncovered from each SAXS pattern. The new information was subsequently used to define features that allowed for classification. Several calculations were tested to extract useful features from the wavelet decomposition maps. The wavelet map average intensity feature was selected as the most promising feature. The wavelet map intensity feature was improved by using pre-processing to remove the high central intensities from the SAXS patterns, and by using different wavelet bases for the wavelet decomposition. The investigation undertaken for this project showed very promising results. A classification rate of 100% was achieved for distinguishing between normal samples and tumor samples. The system also showed promising results when tested on unrelated MRI data. In the future, the semi-automatic pattern recognition tool developed for this project could be automated. With a larger set of data for training and testing, the tool could be improved upon and used to assist radiologists in the detection and classification of breast lesions

A Study on the Improvement of Security Image Analysis Capability Using Artificial Intelligence

The mining of patient data in the health care industry is becoming an increasingly important field because of the direct effect it has on the lives of patients. In the field of medicine, one use of data mining is the early diagnosis of medical diagnostic conditions. However, extracting information from medical records is a laborious process that involves a lot of time and effort. Communities that are dominated by females have an elevated risk of developing breast cancer. Even though mammography is one of the most common ways to use computer-assisted diagnostics, there is still a chance that breast cancer will not be found even if it is one of the most common ways to find and screen for the disease. This indicates that just thirty percent of breast cancers are diagnosed at the appropriate time. Digital image pre-processing includes grayscale-to-binary conversion, noise reduction, and character separation. Most picture recognition algorithms employ statistical, syntactic, and template matching. Neural networks and support vector machines have enabled recent photo identification advances. This article discusses the second stage of the pre-processing procedure, which is adding a filter to the image after it has been segmented in order to make it seem more appealing. It works to identify the area of interest and improve the image by removing the breast border in order to apply filtering algorithms. The breast image\u27s edge is reconstructed using morphological processes in the segmentation method that has been proposed, and breast masses are found by subtracting the two images. In addition, a modified bi-level histogram and homomorphic filters were used in order to improve the image\u27s quality by reducing noise and enhancing contrast

Multirotor UAV Design and Development – Case Study

This paper proposes the development and production of multirotor UAV parts using additive manufacturing. A new smart design approach is needed to take advantage of additive manufacturing in terms of reducing the product weight and making the product more customizable and specific purpose-oriented while also reducing the time and cost of product development and production. This paper provides a brief overview of three additive technologies: fused deposition modelling, stereolithography, and selective laser sintering. Two different UAV modules, the avionics module and GPS holder assembly, are described and produced. Also, some design ideas and approaches are explained, such as snap-fit joints and thread joints using hex bolt pockets and metal screws. The goal of this paper is to develop and manufacture special purpose UAV parts that are durable, sustainable, and low cost. For this purpose, the additive manufacturing process is proposed and described, from the idea to the final product

Shape description and matching using integral invariants on eccentricity transformed images

Matching occluded and noisy shapes is a problem frequently encountered in medical image analysis and more generally in computer vision. To keep track of changes inside the breast, for example, it is important for a computer aided detection system to establish correspondences between regions of interest. Shape transformations, computed both with integral invariants (II) and with geodesic distance, yield signatures that are invariant to isometric deformations, such as bending and articulations. Integral invariants describe the boundaries of planar shapes. However, they provide no information about where a particular feature lies on the boundary with regard to the overall shape structure. Conversely, eccentricity transforms (Ecc) can match shapes by signatures of geodesic distance histograms based on information from inside the shape; but they ignore the boundary information. We describe a method that combines the boundary signature of a shape obtained from II and structural information from the Ecc to yield results that improve on them separately

BREAST CANCER DETECTION USING COMPUTATIONAL INTELLIGENCE

Mammograms are the best tool to detect an early disease of breast cancer. In mammography, medical experts look for clustered microcalcifications and irregular density masses. As microcalcification is a tiny speck of calcium in breast, it appears as white spot in mammogram. Problem occurred when the clinician reads the mammograms using a magnifying glass, as it is difficult to detect calcification because there is a wide range of abnormalities and it also due to the small size and their similarity with other tissue structure. One of the problems is to distinguish between malignant and benign tumors. Thus, the objectives of this project are to enhance mammogram image using image processing technique and to provide a pattern recognition system by signifying whether further investigation is needed, therefore it may assist medical expert in detection of breast cancer. Accordingly, the scope of this project is based on the pattern recognition system, which includes preprocessing, feature extraction, and classification. The task for the project is divided into two parts. The first part is the enhancement of the image and the detection of calcification. The second part of the project is to design, develop, and test the network whether it run as expected. As the result, mammogram images have been processed through image processing by using MATLAB, and opening morphological operation has been used for the detection. A pattern recognition system has been developed by the use of neural network. As a conclusion, a successful implementation of pattern recognition system as one way to detect breast cancer could help medical field in diagnosing breast cancer

BREAST CANCER DETECTION USING COMPUTATIONAL INTELLIGENCE

Mammograms are the best tool to detect an early disease of breast cancer. In mammography, medical experts look for clustered microcalcifications and irregular density masses. As microcalcification is a tiny speck of calcium in breast, it appears as white spot in mammogram. Problem occurred when the clinician reads the mammograms using a magnifying glass, as it is difficult to detect calcification because there is a wide range of abnormalities and it also due to the small size and their similarity with other tissue structure. One of the problems is to distinguish between malignant and benign tumors. Thus, the objectives of this project are to enhance mammogram image using image processing technique and to provide a pattern recognition system by signifying whether further investigation is needed, therefore it may assist medical expert in detection of breast cancer. Accordingly, the scope of this project is based on the pattern recognition system, which includes preprocessing, feature extraction, and classification. The task for the project is divided into two parts. The first part is the enhancement of the image and the detection of calcification. The second part of the project is to design, develop, and test the network whether it run as expected. As the result, mammogram images have been processed through image processing by using MATLAB, and opening morphological operation has been used for the detection. A pattern recognition system has been developed by the use of neural network. As a conclusion, a successful implementation of pattern recognition system as one way to detect breast cancer could help medical field in diagnosing breast cancer