Classification of Mammogram Images by Using SVM and KNN

Breast cancer is a fairly diverse illness that affects a large percentage of women in the west. A mammogram is an X-ray-based evaluation of a woman's breasts to see if she has cancer. One of the earliest prescreening diagnostic procedures for breast cancer is mammography. It is well known that breast cancer recovery rates are significantly increased by early identification. Mammogram analysis is typically delegated to skilled radiologists at medical facilities. Human mistake, however, is always a possibility. Fatigue of the observer can commonly lead to errors, resulting in intraobserver and interobserver variances. The image quality affects the sensitivity of mammographic screening as well. The goal of developing automated techniques for detection and grading of breast cancer images is to reduce various types of variability and standardize diagnostic procedures. The classification of breast cancer images into benign (tumor increasing, but not harmful) and malignant (cannot be managed, it causes death) classes using a two-way classification algorithm is shown in this study. The two-way classification data mining algorithms are utilized because there are not many abnormal mammograms. The first classification algorithm, k-means, divides a given dataset into a predetermined number of clusters. Support Vector Machine (SVM), a second classification algorithm, is used to identify the optimal classification function to separate members of the two classes in the training dat

Can high-frequency ultrasound predict metastatic lymph nodes in patients with invasive breast cancer?

Aim To determine whether high-frequency ultrasound can predict the presence of metastatic axillary lymph nodes, with a high specificity and positive predictive value, in patients with invasive breast cancer. The clinical aim is to identify patients with axillary disease requiring surgery who would not normally, on clinical grounds, have an axillary dissection, so potentially improving outcome and survival rates. Materials and methods The ipsilateral and contralateral axillae of 42 consecutive patients with invasive breast cancer were scanned prior to treatment using a B-mode frequency of 13 MHz and a Power Doppler frequency of 7 MHz. The presence or absence of an echogenic centre for each lymph node detected was recorded, and measurements were also taken to determine the L/S ratio and the widest and narrowest part of the cortex. Power Doppler was also used to determine vascularity. The contralateral axilla was used as a control for each patient. Results In this study of patients with invasive breast cancer, ipsilateral lymph nodes with a cortical bulge ≥3 mm and/or at least two lymph nodes with absent echogenic centres indicated the presence of metastatic axillary lymph nodes (10 patients). The sensitivity and specificity were 52.6% and 100%, respectively, positive and negative predictive values were 100% and 71.9%, respectively, the P value was 0.001 and the Kappa score was 0.55.\ud Conclusion This would indicate that high-frequency ultrasound can be used to accurately predict metastatic lymph nodes in a proportion of patients with invasive breast cancer, which may alter patient management

Computer-aided detection and diagnosis of breast cancer in 2D and 3D medical imaging through multifractal analysis

This Thesis describes the research work performed in the scope of a doctoral research program and presents its conclusions and contributions. The research activities were carried on in the industry with Siemens S.A. Healthcare Sector, in integration with a research team. Siemens S.A. Healthcare Sector is one of the world biggest suppliers of products, services and complete solutions in the medical sector. The company offers a wide selection of diagnostic and therapeutic equipment and information systems. Siemens products for medical imaging and in vivo diagnostics include: ultrasound, computer tomography, mammography, digital breast tomosynthesis, magnetic resonance, equipment to angiography and coronary angiography, nuclear imaging, and many others. Siemens has a vast experience in Healthcare and at the beginning of this project it was strategically interested in solutions to improve the detection of Breast Cancer, to increase its competitiveness in the sector. The company owns several patents related with self-similarity analysis, which formed the background of this Thesis. Furthermore, Siemens intended to explore commercially the computer- aided automatic detection and diagnosis eld for portfolio integration. Therefore, with the high knowledge acquired by University of Beira Interior in this area together with this Thesis, will allow Siemens to apply the most recent scienti c progress in the detection of the breast cancer, and it is foreseeable that together we can develop a new technology with high potential. The project resulted in the submission of two invention disclosures for evaluation in Siemens A.G., two articles published in peer-reviewed journals indexed in ISI Science Citation Index, two other articles submitted in peer-reviewed journals, and several international conference papers. This work on computer-aided-diagnosis in breast led to innovative software and novel processes of research and development, for which the project received the Siemens Innovation Award in 2012. It was very rewarding to carry on such technological and innovative project in a socially sensitive area as Breast Cancer.No cancro da mama a deteção precoce e o diagnóstico correto são de extrema importância na prescrição terapêutica e caz e e ciente, que potencie o aumento da taxa de sobrevivência à doença. A teoria multifractal foi inicialmente introduzida no contexto da análise de sinal e a sua utilidade foi demonstrada na descrição de comportamentos siológicos de bio-sinais e até na deteção e predição de patologias. Nesta Tese, três métodos multifractais foram estendidos para imagens bi-dimensionais (2D) e comparados na deteção de microcalci cações em mamogramas. Um destes métodos foi também adaptado para a classi cação de massas da mama, em cortes transversais 2D obtidos por ressonância magnética (RM) de mama, em grupos de massas provavelmente benignas e com suspeição de malignidade. Um novo método de análise multifractal usando a lacunaridade tri-dimensional (3D) foi proposto para classi cação de massas da mama em imagens volumétricas 3D de RM de mama. A análise multifractal revelou diferenças na complexidade subjacente às localizações das microcalci cações em relação aos tecidos normais, permitindo uma boa exatidão da sua deteção em mamogramas. Adicionalmente, foram extraídas por análise multifractal características dos tecidos que permitiram identi car os casos tipicamente recomendados para biópsia em imagens 2D de RM de mama. A análise multifractal 3D foi e caz na classi cação de lesões mamárias benignas e malignas em imagens 3D de RM de mama. Este método foi mais exato para esta classi cação do que o método 2D ou o método padrão de análise de contraste cinético tumoral. Em conclusão, a análise multifractal fornece informação útil para deteção auxiliada por computador em mamogra a e diagnóstico auxiliado por computador em imagens 2D e 3D de RM de mama, tendo o potencial de complementar a interpretação dos radiologistas

Determinants and influence of mammographic features on breast cancer risk

Mammographic density and mammographic microcalcifications are the key imaging features in mammography examination. Mammographic density is known as a strong risk factor for breast cancer and is the radiographic appearance of epithelial and fibrous tissue which appears white on a mammogram. While, the dark part of a mammogram represents the fatty tissue. Mammographic microcalcifications appear as small deposits of calcium and they are one of the earliest sign of breast cancer. Malignant microcalcifications are seen in both in situ and invasive lesions. In this thesis we used the data from the prospective KARMA cohort to study the association between established breast cancer risk factors with mammographic density change over time (Study I), to examine the association between annual mammographic density change and risk of breast cancer (Study II), to investigate the association between established risk factors for breast cancer and microcalcification clusters and their asymmetry (Study III), and finally to elucidate the association between microcalcification clusters, their asymmetry, and risk of overall and subtype specific breast cancer (Study IV). The lifestyle and reproductive factors were assessed using web-based questionnaires. Average mammographic density and total microcalcification clusters were measured using a Computer Aided Detection system (CAD) and the STRATUS method, respectively. In Study I, the average yearly dense area change was -1.0 cm . Body mass index (BMI) and physical activity were statistically associated with density change. Beside age, lean and physically active women had the largest decrease in mammographic density per year. In Study II, overall, 563 women were diagnosed with breast cancer and annual mammographic density change did not seem to influence the risk of breast cancer. Furthermore, density change does not seem to modify the association between baseline density and risk of breast cancer. In Study III, age, mammographic density, genetic factors related to breast cancer, having more children, longer duration of breast-feeding were significantly associated with increased risk of presence of microcalcification clusters. In Study IV, 676 women were diagnosed with breast cancer. Further, women with 33 microcalcification clusters had 2 times higher risk of breast cancer compared to women with no clusters. Microcalcification clusters were associated with both in situ and invasive breast cancer. Finally, during postmenopausal period, microcalcification clusters influence risk of breast cancer to the similar extend as baseline mammographic density. In conclusion, we have identified novel determinants of mammographic density changes and potential predictors of suspicious mammographic microcalcification clusters. Further, our results suggested that annual mammographic density change does not influence breast cancer risk, while presence of suspicious microcalcification clusters was strongly associated with breast cancer risk

Precision Imaging Ultrasound Technology - Does It Improve Accuracy And Increase Confidence In Diagnosing Breast Tumours?

Objective To determine the effect of Precision Imaging (PI), an innovative speckle reduction algorithm, on the diagnostic efficacy in breast ultrasound Material and methods Patients aged from 20 to 84 years screened by the breast clinic from October 2010 to June 2011 were included in this research. The commercial ultrasound scanner Toshiba AplioMX, with compact linear transducers 15-7MHz and 12-5 MHz was used for image acquisition. A single projection image that was considered to best represent the lesion was recorded without PI (L0), then with all other 3 levels of PI, namely Precision 1 (L1), Precision 2 (L2) and Precision 3 (L3), with higher numbers signifying greater speckle reduction. Fifty one breast lesions (20 malignant and 31 benign) were selected from over 200 collected lesions, with selection criteria based on the 1- 5 classification system developed by National Breast Cancer Centre in collaboration with the Royal Australian and New Zealand College of Radiologists. These selected images were cropped to remove the technical details, which included patient information as well as PI level. These processed images were then organised into four sets (A,B,C,D) with images in same PI level. These four sets of images were evaluated by six radiologists and six sonographers dedicated to breast imaging, scoring each lesion between 1 and 6.These scores were subjected to Q-Perform software, DBMMRMC, Mann-Whitney U-test, Wilcoxon Signed rank test and IBM SPSS statistics for statistical analyses. Results The overall means ROCAUC for L0 was 0.79, L1 was 0.80, L2 was 0.81, and L3 was 0.81. The overall means sensitivity for L0 was 0.75, L1 was 0.79, L2 was 0.80, and L3 was 0.78.Overall means specificity for L0 was 0.74, L1 was 0.72, L2 was 0.73, and L3 was 0.71. Conclusion The data analysis on ROC, sensitivity, and specificity did not demonstrate any significant improvement in diagnostic efficacy amongst expert observers in this study

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

A Novel Hybrid K-Means and GMM Machine Learning Model for Breast Cancer Detection

Breast cancer is the second leading cause of death among a large number of women worldwide. It may be challenging for radiologists to diagnose and treat breast cancer. Consequently, primary care improves disease prevention and death. Early detection increases treatment options and saves life, which is the major target of this research. This research indicates the versatility of the methodology by integrating contemporary segmentation approaches with machine learning methods, which are developing areas of research. In the pre-processing process, an adaptive median filter is utilized for noise removal, enhancement of image quality, conservation of edges, and smoothing. This research makes a significant contribution by proposing a new parameter for evaluating K-means and a Gaussian mixture model (GMM) performance. A hybrid combination of segmentation and detection was applied to breast cancer. The proposed technique is significant for classifying benign and malignant tumors. The simulated results are discussed and evaluated to determine the competence of this method for the early diagnosis of breast cancer. This method allows medical experts to recognize breast cancer at a faster rate and provide higher accuracy. An ANOVA test was used to determine the multi-variant analysis and prediction rate for the proposed method