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Proceedings Paper

Random forest learning of ultrasonic statistical physics and object spaces for lesion detection in 2D sonomammography
Author(s): Debdoot Sheet; Athanasios Karamalis; Silvan Kraft; Peter B. Noël; Tibor Vag; Anup Sadhu; Amin Katouzian; Nassir Navab; Jyotirmoy Chatterjee; Ajoy K. Ray
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Paper Abstract

Breast cancer is the most common form of cancer in women. Early diagnosis can significantly improve lifeexpectancy and allow different treatment options. Clinicians favor 2D ultrasonography for breast tissue abnormality screening due to high sensitivity and specificity compared to competing technologies. However, inter- and intra-observer variability in visual assessment and reporting of lesions often handicaps its performance. Existing Computer Assisted Diagnosis (CAD) systems though being able to detect solid lesions are often restricted in performance. These restrictions are inability to (1) detect lesion of multiple sizes and shapes, and (2) differentiate between hypo-echoic lesions from their posterior acoustic shadowing. In this work we present a completely automatic system for detection and segmentation of breast lesions in 2D ultrasound images. We employ random forests for learning of tissue specific primal to discriminate breast lesions from surrounding normal tissues. This enables it to detect lesions of multiple shapes and sizes, as well as discriminate between hypo-echoic lesion from associated posterior acoustic shadowing. The primal comprises of (i) multiscale estimated ultrasonic statistical physics and (ii) scale-space characteristics. The random forest learns lesion vs. background primal from a database of 2D ultrasound images with labeled lesions. For segmentation, the posterior probabilities of lesion pixels estimated by the learnt random forest are hard thresholded to provide a random walks segmentation stage with starting seeds. Our method achieves detection with 99.19% accuracy and segmentation with mean contour-to-contour error < 3 pixels on a set of 40 images with 49 lesions.

Paper Details

Date Published: 29 March 2013
PDF: 8 pages
Proc. SPIE 8675, Medical Imaging 2013: Ultrasonic Imaging, Tomography, and Therapy, 867515 (29 March 2013); doi: 10.1117/12.2006370
Show Author Affiliations
Debdoot Sheet, Technische Univ. München (Germany)
Indian Institute of Technology Kharagpur (India)
Athanasios Karamalis, Technische Univ. München (Germany)
Silvan Kraft, Technische Univ. München (Germany)
Peter B. Noël, Technische Univ. München (Germany)
Tibor Vag, Technische Univ. München (Germany)
Anup Sadhu, Medical College and Hospital Kolkata (India)
Amin Katouzian, Technische Univ. München (Germany)
Nassir Navab, Technische Univ. München (Germany)
Jyotirmoy Chatterjee, Indian Institute of Technology Kharagpur (India)
Ajoy K. Ray, Indian Institute of Technology Kharagpur (India)

Published in SPIE Proceedings Vol. 8675:
Medical Imaging 2013: Ultrasonic Imaging, Tomography, and Therapy
Johan G. Bosch; Marvin M. Doyley, Editor(s)

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