Computer Aided Detection of Microcalcifications Utilizing Texture Analysis

A comparative study of texture measures for the classification of breast tissue is presented. The texture features investigated include Angular Second Moments, Power Spectrum Analysis and a novel feature, Laws Energy Ratios. The texture study was accomplished as part of the development of a Model Based Vision (MBV) system for the automatic detection of microcalcifications. An overview of the Microcalcification Detection System is presented, which applies image differencing techniques, feature selection methods, and neural networks for locating microcalcification clusters in mammograms. The Power Spectrum Analysis feature set had the best overall performance with an 83% Probability of Detection and an average False ROl Rate of 2.17 ROIs per image over 53 mammograms. A combination of Laws Energy Ratio and Power Spectrum Analysis features selected using Ruck Saliency metrics achieved an increased Probability of Detection of 85% with an average 4 false ROIs per image

OTOMATIS DETEKSI DAN KLASIFIKASI MASSA PADA MAMMOGRAM

Kanker Payudara tetap menjadi masalah kesehatan masyarakat yang sangat signifikan di dunia. Deteksi dini adalah kunci utama bagi memperbaiki prognosis kanker payudara. Mammography telah menjadi salah satu metode yang paling dapat diandalkan bagi deteksi dini dari karsinoma payudara. Akan tetapi, hal ini sulit bagi radiologis untuk memberikan penilaian yang akurat dan tepat dalam menyeragamkan hasil mammogram dalam lingkup yang sukar dan luas. Hasil yang dapat diperkirakan radiologis pada skrining kanker payudara ini hanya 75 persen keakuratannya. Diharapkan dapat langsung mendiagnosis pemicu kelainan pada lokasi kanker tersebut. Kanker payudara system CAD seperti itu dapat menolong dan memberikan yang sangat diperlukan untuk mengontrol kanker payudara. Microcalfication dan massa adalah dua indicator paling penting dari penyakit berbahaya, dan deteksi otomatis sangat berharga untuk diagnosis dini pada kanker payudara. Karena biasanya massa yan tidak dapat dibedakan dari sekitar parenchymal, dengan mendekteksi dan mengklasifikasikan massa secara otomatis maka akan lebih menantang. Makalah ini membahas metode untuk deteksi dan klasifikasi massa, dan membandingkan keuntungan dan kekurangan mereka. Keywords: Mass; Mammogram; CAD; Wavelet; Fuzzy logic; Contrast enhancement; Feature selectio

Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

Elemental and phase composition of breast calcifications

Despite the importance of calcifications in early detection of breast cancer, and their proposed association with tumour growth, remarkably little detail is known about their chemical composition, or how this relates to pathology. One reason for this gap is the difficulty of systematically and precisely locating calcifications for analysis, particularly in sections taken from diagnostic archives. Two simple methods were developed which can achieve this in sections cut from wax embedded breast tissue. These are based on micro-CT and x-ray fluoroscopy mapping, and were used to locate calcifications for further study. The elemental composition of calcifications in histological sections was measured using energy-dispersive x-ray spectroscopy in an environmental scanning electron microscope. Variations in Ca:P ratio could in principle be detected non-invasively by dual energy absorptiometry, as demonstrated in a proof of principle experiment. However, the Ca:P ratio was found to lie in a narrow range similar to bone, with no significant difference between benign and malignant. In contrast, a substantial and significant difference in Na:Ca ratio was found between benign and malignant specimens. This has potential for revealing malignant changes in the vicinity of a core needle biopsy. The phase composition and crystallographic parameters within calcifications was measured using synchrotron x-ray diffraction. This is the first time crystallite size and lattice parameters have been measured in breast calcifications, and it was found that these both parallel closely the changes in these parameters with age observed in foetal bone. It was also discovered that these calcifications contain a small proportion of magnesium whitlockite, and that this proportion increases from benign, to carcinoma in-situ, to invasive cancer. When combined with other recent evidence on the effect of magnesium on hydroxyapatite precipitation, this suggests a mechanism explaining observations that carbonate levels within breast calcifications are lower in malignant specimens

A cell level automated approach for quantifying antibody staining in immunohistochemistry images. A structural approach for quantifying antibody staining in colonic cancer spheroid images by integrating image processing and machine learning towards the implementation of computer aided scoring of cancer markers.

Immunohistological (IHC) stained images occupy a fundamental role in the pathologist¿s diagnosis and monitoring of cancer development. The manual process of monitoring such images is a subjective, time consuming process that typically relies on the visual ability and experience level of the pathologist. A novel and comprehensive system for the automated quantification of antibody inside stained cell nuclei in immunohistochemistry images is proposed and demonstrated in this research. The system is based on a cellular level approach, where each nucleus is individually analyzed to observe the effects of protein antibodies inside the nuclei. The system provides three main quantitative descriptions of stained nuclei. The first quantitative measurement automatically generates the total number of cell nuclei in an image. The second measure classifies the positive and negative stained nuclei based on the nuclei colour, morphological and textural features. Such features are extracted directly from each nucleus to provide discriminative characteristics of different stained nuclei. The output generated from the first and second quantitative measures are used collectively to calculate the percentage of positive nuclei (PS). The third measure proposes a novel automated method for determining the staining intensity level of positive nuclei or what is known as the intensity score (IS). The minor intensity features are observed and used to classify low, intermediate and high stained positive nuclei. Statistical methods were applied throughout the research to validate the system results against the ground truth pathology data. Experimental results demonstrate the effectiveness of the proposed approach and provide high accuracy when compared to the ground truth pathology data

An image processing decisional system for the Achilles tendon using ultrasound images

The Achilles Tendon (AT) is described as the largest and strongest tendon in the human body. As for any other organs in the human body, the AT is associated with some medical problems that include Achilles rupture and Achilles tendonitis. AT rupture affects about 1 in 5,000 people worldwide. Additionally, AT is seen in about 10 percent of the patients involved in sports activities. Today, ultrasound imaging plays a crucial role in medical imaging technologies. It is portable, non-invasive, free of radiation risks, relatively inexpensive and capable of taking real-time images. There is a lack of research that looks into the early detection and diagnosis of AT abnormalities from ultrasound images. This motivated the researcher to build a complete system which enables one to crop, denoise, enhance, extract the important features and classify AT ultrasound images. The proposed application focuses on developing an automated system platform. Generally, systems for analysing ultrasound images involve four stages, pre-processing, segmentation, feature extraction and classification. To produce the best results for classifying the AT, SRAD, CLAHE, GLCM, GLRLM, KPCA algorithms have been used. This was followed by the use of different standard and ensemble classifiers trained and tested using the dataset samples and reduced features to categorize the AT images into normal or abnormal. Various classifiers have been adopted in this research to improve the classification accuracy. To build an image decisional system, a 57 AT ultrasound images has been collected. These images were used in three different approaches where the Region of Interest (ROI) position and size are located differently. To avoid the imbalanced misleading metrics, different evaluation metrics have been adapted to compare different classifiers and evaluate the whole classification accuracy. The classification outcomes are evaluated using different metrics in order to estimate the decisional system performance. A high accuracy of 83% was achieved during the classification process. Most of the ensemble classifies worked better than the standard classifiers in all the three ROI approaches. The research aim was achieved and accomplished by building an image processing decisional system for the AT ultrasound images. This system can distinguish between normal and abnormal AT ultrasound images. In this decisional system, AT images were improved and enhanced to achieve a high accuracy of classification without any user intervention