IPES: An Image Processing-Enabled Expert System for the Detection of Breast Malignant Tumors

Mammography is an influential screening tool for preliminary detection of breast carcinoma. However, Interpreting mammograms is exhausting, particularly in the screening context. Moreover, sensitivity of mammography based screening is influenced by image quality and the experience level of radiologist. Thus, computer-aided diagnosis (CAD) programs can be utilized as a second-opinion tools that enhance the performance of radiologists, by consolidating sensitivity rates in contrast with those taken by double readings. The current paper is directed towards the integration of image processing and the rule-based reasoning into a diagnostic expert system for breast tumors. A proposed system termed IPES (Image Processing-enabled Expert System) is developed for the detection of breast malignant tumors, appear in mammography in three steps: (1) segmentation of mammographic masses from both pectoral muscle region and breast tissues, (2) Characterization of segmented masses upon the standards of Breast Imaging Reporting and Data System (BI-RADS) by: (i) shape-relevant features; (ii) margin characteristics; and (iii) density features, and (3) diagnosis of mass type upon some inference rules. The data set used for testing IPES contained 540 samples obtained from the standard Digital Database for Screening Mammography (DDSM). The Receiver Operator Characteristic (ROC) curves have been employed to evaluate the sensitivities and specificities of the system. Finally, the results reveal the efficacy of IBES in discriminating both malignant and benign breast masses.

[1]  Mirko Perkusich,et al.  Early diagnosis of gastrointestinal cancer by using case-based and rule-based reasoning , 2016, Expert Syst. Appl..

[2]  Ekta Walia,et al.  Variants of dense descriptors and Zernike moments as features for accurate shape-based image retrieval , 2014, Signal Image Video Process..

[3]  Ekta Walia,et al.  Zernike moments and LDP-weighted patches for content-based image retrieval , 2014, Signal Image Video Process..

[4]  Wenjun Chris Zhang,et al.  An Expert Support System for Breast Cancer Diagnosis using Color Wavelet Features , 2012, Journal of Medical Systems.

[5]  Miguel Ángel Guevara-López,et al.  Discovering Mammography-based Machine Learning Classifiers for Breast Cancer Diagnosis , 2012, Journal of Medical Systems.

[6]  Robert Ivor John,et al.  Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models , 2012, J. Biomed. Informatics.

[7]  Xiaohui Liu,et al.  Mammogram retrieval on similar mass lesions , 2012, Comput. Methods Programs Biomed..

[8]  Kidiyo Kpalma,et al.  Shape-Based Invariant Feature Extraction for Object Recognition , 2012 .

[9]  Lihua Li,et al.  An Interactive System for Computer-Aided Diagnosis of Breast Masses , 2012, Journal of Digital Imaging.

[10]  Karla Kerlikowske,et al.  Influence of annual interpretive volume on screening mammography performance in the United States. , 2011, Radiology.

[11]  Zongmin Ma,et al.  Shape feature descriptor using modified Zernike moments , 2011, Pattern Analysis and Applications.

[12]  D. Evans,et al.  Assessing women at high risk of breast cancer: a review of risk assessment models. , 2010, Journal of the National Cancer Institute.

[13]  Heng-Da Cheng,et al.  Detection and classification of masses in breast ultrasound images , 2010, Digit. Signal Process..

[14]  Mohammed Bahoura,et al.  Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes , 2009, Comput. Biol. Medicine.

[15]  Anselmo Cardoso de Paiva,et al.  Detection of Breast Masses in Mammogram Images Using Growing Neural Gas Algorithm and Ripley’s K Function , 2009, J. Signal Process. Syst..

[16]  M. Cevdet Ince,et al.  An expert system for detection of breast cancer based on association rules and neural network , 2009, Expert Syst. Appl..

[17]  C. Schmid,et al.  Description of Interest Regions with Center-Symmetric Local Binary Patterns , 2006, ICVGIP.

[18]  Alan C. Bovik,et al.  Computer-Aided Detection and Diagnosis in Mammography , 2005 .

[19]  Radhika Sivaramakrishna,et al.  Texture analysis of lesions in breast ultrasound images. , 2002, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[20]  A. Stavros,et al.  Breast biopsy avoidance: the value of normal mammograms and normal sonograms in the setting of a palpable lump. , 2001, Radiology.

[21]  Y Fainman,et al.  Wave-front generation of Zernike polynomial modes with a micromachined membrane deformable mirror. , 1999, Applied optics.

[22]  Stephen M. Smith,et al.  SUSAN—A New Approach to Low Level Image Processing , 1997, International Journal of Computer Vision.

[23]  Izak Benbasat,et al.  The Use and Effects of Knowledge-Based System Explanations: Theoretical Foundations and a Framework for Empirical Evaluation , 1996, Inf. Syst. Res..

[24]  R. Warren,et al.  Mammography screening: an incremental cost effectiveness analysis of double versus single reading of mammograms , 1996, BMJ.

[25]  R. Hendrick,et al.  Imaging of the radiographically dense breast. , 1993, Radiology.

[26]  Yue Li,et al.  Content-Based Retrieval for Mammograms , 2009 .