Comparative of shape and texture features in classifications of breast masses in digitized mammograms

Sergio Koodi Kinoshita; Paulo M. Azevedo Marques; Annie France Frere; Heitor R. C. Marana; Ricardo Jose Ferrari; Roberto Rodrigues Pereira Jr.

doi:10.1117/12.387752

6 June 2000 Comparative of shape and texture features in classifications of breast masses in digitized mammograms

Sergio Koodi Kinoshita, Paulo M. Azevedo Marques, Annie France Frere, Heitor R. C. Marana, Ricardo Jose Ferrari, Roberto Rodrigues Pereira Jr.

Author Affiliations +

Proceedings Volume 3979, Medical Imaging 2000: Image Processing; (2000) https://doi.org/10.1117/12.387752
Event: Medical Imaging 2000, 2000, San Diego, CA, United States

Abstract

The aim of this work was to determine a methodology to selection of the best features subset and artificial neural network (ANN) topology to classify masses lesions. The backpropagation training algorithm was used to adjust the weights of ANN. A total of 118 regions of interest images were chosen (68 benign and 50 malignant lesions). In a first step, images were submitted to a combined process of thresholding, mathematical morphology, and region growing techniques. After, fourteen texture features (Haralick descriptors) and fourteen shape features (circularity, compactness, Gupta descriptors, Shen descriptors, Hu descriptors, Fourier descriptor and Wee descriptors) were extracted. The Jeffries-Matusita method was used to select the best features. Three shape features sets and three texture features sets were selected. The Receiver Operating Characteristic (ROC) analyses were conducted to evaluated the classifier performance. The best result for shape feature set was accurate classification rate of 98.21%, specificity of 98.37%, sensitivity of 98.00% and the area under ROC curve of 0.99, for a ANN with 5 hidden units. The best result for texture feature set was accurate classification rate of 97.08%, specificity of 98.53%, sensitivity of 95.11% and the area under ROC curve of 0.98, for an ANN with 4 hidden units.

Citation Download Citation

Sergio Koodi Kinoshita, Paulo M. Azevedo Marques, Annie France Frere, Heitor R. C. Marana, Ricardo Jose Ferrari, and Roberto Rodrigues Pereira Jr. "Comparative of shape and texture features in classifications of breast masses in digitized mammograms", Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); https://doi.org/10.1117/12.387752

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