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

Abnormality Detection in Mammography using Deep Convolutional Neural Networks

Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53\% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.Comment: 6 page

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Detection of microcalcifications in mammograms using error of prediction and statistical measures

A two-stage method for detecting microcalcifications in mammograms is presented. In the first stage, the determination of the candidates for microcalcifications is performed. For this purpose, a 2-D linear prediction error filter is applied, and for those pixels where the prediction error is larger than a threshold, a statistical measure is calculated to determine whether they are candidates for microcalcifications or not. In the second stage, a feature vector is derived for each candidate, and after a classification step using a support vector machine, the final detection is performed. The algorithm is tested with 40 mammographic images, from Screen Test: The Alberta Program for the Early Detection of Breast Cancer with 50- m resolution, and the results are evaluated using a freeresponse receiver operating characteristics curve. Two different analyses are performed: an individual microcalcification detection analysis and a cluster analysis. In the analysis of individual microcalcifications, detection sensitivity values of 0.75 and 0.81 are obtained at 2.6 and 6.2 false positives per image, on the average, respectively. The best performance is characterized by a sensitivity of 0.89, a specificity of 0.99, and a positive predictive value of 0.79. In cluster analysis, a sensitivity value of 0.97 is obtained at 1.77 false positives per image, and a value of 0.90 is achieved at 0.94 false positive per imag

Predicting risk of malignancy in patients with indeterminate thyroid nodules

Thyroid cancer is the most prevalent endocrine cancer (1). The prevalence of palpable thyroid nodules in the general adult population is 4% to 7% (2). Ultrasound imaging detects thyroid nodules in 19%-68% of randomly selected individuals (3). The rate of thyroid cancer in nodules found on US is 4% to 15% (4). In order to evaluate thyroid nodules patients undergo thyroid ultrasonography and, if needed, a fine-needle aspiration biopsy. Of all fine-needle aspiration biopsies, 15-30% are indeterminate on cytology (5). While only 3% of these nodules are malignant on average, a much higher percentage of nodules are surgically removed in order to rule out malignancy after indeterminate FNA results. Our goal is to identify clinical and ultrasound predictors of benign results in indeterminate nodules, to assist physicians in selecting nodules for surgical removal versus monitoring with ultrasound imaging. Between October 2010 and November 2017 there were 129 patients with 134 thyroid nodules from Temple University Hospital, Jeanes Hospital, and Fox Chase Cancer Center who had a total or partial thyroidectomy after a cytology report of at least one AUS or FLUS thyroid nodule. These patients were evaluated for age, sex, BMI, TSH, fT4, tT3, nodule size, and ultrasonography features to determine if any features were predictive of a benign or malignant thyroid nodule. Additionally, we looked at whether any of these features were more likely to occur in an AUS nodule or a FLUS nodule. We found that none of the demographic factors, thyroid function tests, or ultrasound features were good predictors of malignancy in AUS or FLUS thyroid nodules. We found that AUS nodules are more likely to be malignant than FLUS nodules, and this held true when we accounted for age, sex, smoking history, and BMI. We concluded that demographic factors and thyroid function tests are unable to predict increased risk of malignancy in Bethesda category III nodules, AUS nodules are more likely to be malignant that FLUS nodules, and nodules with at least one suspicious ultrasound feature are more likely to be AUS nodules than FLUS nodules due to AUS nodules having nuclear atypia and FLUS nodules having architectural atypia

Detection of Microcalcification Using Mammograms

Mammography is one of the most common and useful techniques used for early detection of the breast cancer. It is the low-dose x-ray examination performed to patient to detect the primary mass when it is still small and confined to the breast. The present of microcalcification is a highly indication of the cancerous tissues. Microcalcification is a tiny specks of calcium deposited in the breast. The problem encountered in detecting the microcalcification by using this method is the limitation of the mammogram image (x-ray) to detect the microcalcification due to mainly to their small size, low contrast, and the similarity of their radiographic appearance to dense tissue. Statistic had shown that approximately 10%-30% of breast cancers retrospectively visible on the mammograms were missed ormisinterpreted due tohuman or technical factors [1]. This project focuses on the enhancement of the mammograms image by applying the image processing techniques to assist doctors in detecting the breast cancer disease. The aim is to provide a low-cost technology in detecting the breast cancer at the early stage. This project develops the program using MATLAB and Borland C++ to enhance the digitized mammograms image by using the image processing technique. The mammogram is first digitized and processed by the program developed to detect the microcalcification deposited in the breast. The morphological operation was a simple and suitablemethod in identifying the microcalcification. The top-hat algorithm method that is a morphological operation was developed using MATLAB and successfully obtained the output image that shows the candidate microcalcification. The top hat method consists of four stages which are digitization of mammograms, image enhancement, image segmentation and feature extraction. Various image processing techniques were applied including filters, histogram generation, thresholding and edge detection. The top hat method was applied to mammograms samples of eight patients and able to detect the microcalcification. The results obtained were defined into three categories, below expectation, meet expectation and above expectation. In conclusion, the project had met an acceptable degree ofaccuracy level