thermogram Breast Cancer Detection : a comparative study of two machine learning techniques

Breast cancer is considered one of the major threats for women’s health all over the world. The World Health Organization (WHO) has reported that 1 in every 12 women could be subject to a breast abnormality during her lifetime. To increase survival rates, it is found that it is very effective to early detect breast cancer. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with patients with dense breast nor with tumor size less than 2 mm. Thermography-based breast cancer approach can address these problems. In this paper, a thermogram-based breast cancer detection approach is proposed. This approach consists of four phases: (1) Image Pre-processing using homomorphic filtering, top-hat transform and adaptive histogram equalization, (2) ROI Segmentation using binary masking and K-mean clustering, (3) feature extraction using signature boundary, and (4) classification in which two classifiers, Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP), were used and compared. The proposed approach is evaluated using the public dataset, DMR-IR. Various experiment scenarios (e.g., integration between geometrical feature extraction, and textural features extraction) were designed and evaluated using different measurements (i.e., accuracy, sensitivity, and specificity). The results showed that ELM-based results were better than MLP-based ones with more than 19%

Post-Processing of Low Dose Mammography Images

In mammography, X-ray radiation is used in sufficient doses to be captured on film for cancer diagnosis. A problem lies in the inherent nature of X-rays to cause cancer. The resolution of the images obtained on film is directly related to the radiation dosage. Thus, a trade-off between image quality and radiation exposure is necessary to ensure proper diagnosis without causing cancer. A possible solution is to decrease the dosage of radiation and improve the image quality of mammograms using post- processing methods applied to digitized film images. Image processing techniques that may improve the resolution of images captured at lower doses include crispening, denoising, histogram equalization, and pattern recognition methods. The Wright Patterson Air Force Base Hospital Radiology Department sponsored this research and provided digitized images of the American College of Radiology (ACR) phantom, which is a model for mammogram image quality and classification. Side by side comparisons were performed of high dose images and low-dose images post-processed using the methods mentioned. The result was improved- resolution on mammography images for lower radiation doses. Thus, this research represents progress towards solving a problem that currently plagues mammography: exposure of patients to high doses of cancer- causing radiation to obtain quality mammography images. By improving the image quality of mammography images at lower radiation doses, the problem of cancer induced by high radiation exposure is alleviated

Application of Fractal and Wavelets in Microcalcification Detection

Breast cancer has been recognized as one or the most frequent, malignant tumors in women, clustered microcalcifications in mammogram images has been widely recognized as an early sign of breast cancer. This work is devote to review the application of Fractal and Wavelets in microcalcifications detection

Contrast enhancement by multi-scale adaptive histogram equalization

An approach for contrast enhancement utilizing multi-scale analysis is introduced. Sub-band coefficients were modified by the method of adaptive histogram equalization. To achieve optimal contrast enhancement, the sizes of sub-regions were chosen with consideration to the support of the analysis filters. The enhanced images provided subtle details of tissues that are only visible with tedious contrast/brightness windowing methods currently used in clinical reading. We present results on chest CT data, which shows significant improvement over existing state-of-the-art methods: unsharp masking, adaptive histogram equalization (AHE), and the contrast limited adaptive histogram equalization (CLAHE). A systematic study on 109 clinical chest CT images by three radiologists suggests the promise of this method in terms of both interpretation time and diagnostic performance on different pathological cases. In addition, radiologists observed no noticeable artifacts or amplification of noise that usually appears in traditional adaptive histogram equalization and its variations

Adaptive multiscale processing for contrast enhancement

This paper introduces a novel approach for accomplishing mammographic feature analysis through overcomplete multiresolution representations. We show that efficient representations may be identified from digital mammograms within a continuum of scale space and used to enhance features of importance to mammography. Choosing analyzing functions that are well localized in both space and frequency, results in a powerful methodology for image analysis. We describe methods of contrast enhancement based on two overcomplete (redundant) multiscale representations: (1) Dyadic wavelet transform (2) (phi) -transform. Mammograms are reconstructed from transform coefficients modified at one or more levels by non-linear, logarithmic and constant scale-space weight functions. Multiscale edges identified within distinct levels of transform space provide a local support for enhancement throughout each decomposition. We demonstrate that features extracted from wavelet spaces can provide an adaptive mechanism for accomplishing local contrast enhancement. We suggest that multiscale detection and local enhancement of singularities may be effectively employed for the visualization of breast pathology without excessive noise amplification

Noise reduction on mammographic phantom images

A noise reduction scheme on digitized mammographic phantom images is presented. This algorithm is based on a direct contrast modification method using an optimal function which is obtained by means of the mean squared error as a criterion. Computer simulated images containing objects similar to those observed in the phantom are built to evaluate the performance of the algorithm. Noise reduction results obtained on both simulated and real phantom images show that the developed method may be considered as a good pre-processing step from the point of view of automating phantom film evaluation by means of image processing

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