Mass Classification in Mammography with Morphological Features and Multiple Kernel Learning

Liu, Xiaoming; Liu, Jun; Feng, Zuren

doi:10.1109/icbbe.2011.5780356

Cited by 1 publication

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, given the difficulty of differentiation between benign and malignant patterns, many studies usually combine texture features and geometry to perform this task, such as Sahiner et al (2001), Shi et al (2007), Suganthi and Madheswaran (2010), Varela et al (2006), Mu et al (2008) and Liu et al (2010Liu et al ( , 2011.…”

Section: Introductionmentioning

confidence: 99%

Texture analysis of masses in digitized mammograms using Gleason and Menhinick Diversity Indexes

Rocha

Bráz

Silva

et al. 2014

RBEB

View full text Add to dashboard Cite

Introduction: Breast cancer is the second most common type of cancer in the world, being more common among women and representing 22% of all new cancer cases every year. The sooner it is diagnosed, the better the chances of a successful treatment are. Mammography is one way to detect non-palpable tumors that cause breast cancer. However, it is known that the sensitivity of this exam can vary considerably due to factors such as the specialist's experience, the patient's age and the quality of the images obtained in the exam. The use of computational techniques involving artificial intelligence and image processing has contributed more and more to support the specialists in obtaining a more precise diagnosis. Methods: This paper proposes a methodology that exclusively uses texture analysis to describe features of masses in digitized mammograms.To increase the efficiency of texture feature extraction, the diversity index's capability to detect patterns of species co-occurrence is used. For this purpose, the Gleason and Menhinick indexes are used. Finally, the extracted texture is classified using the Support Vector Machine, looking to differentiate the malignant masses from the benign. Results: The best result was obtained using the Gleason index, with 86.66% accuracy, 90% sensitivity, 83.33% specificity and an area under the ROC Curve (Az) of 0.86. Conclusion: Both indexes showed statistically similar performance; however, the Gleason index was slightly superior.

show abstract