Similarity measurement on digital mammogram classification

Erna Alimudin, Hanung Adi Nugroho, Teguh Bharata Adji

Abstract


Breast cancer is one of the dominant cause of death in the world. The possibility of recovery which is treated at an early step is approaching 95%. Mammography is the gold standard for early detection. In examining mammogram the overall parenchyma pattern of the left and right breast placed side by side for symmetry assessed of left and right breast tissue by radiologist. Thus, in building Computer Aided Diagnosis (CAD) system for screening mammography, it is necessary to adapt the working procedure of the radiologist. In this study, 30 training images and 30 testing images from Klinik Onkologi Kotabaru Yogyakarta were used. The first step was enhancing the image quality with median filter and contrast limited adaptive histogram equalization (CLAHE). Then, feature extraction was processed by histogram-based and by Gray Level Co-Occurrence Matrix (GLCM) based. Furthermore, the similarity measurement process was used to measure the difference value between features on left and right mammogram. This process was intended to appraise symmetry of left and right mammogram as radiologists did in screening mammography. The results of the classification between normal and abnormal images with Error Back Propagation (EBP) algorithm were accuracy 93.3%, sensitivity 100%, and specificity 83.3%.


Keywords


EBP; histogram; GLCM; mammogram; similarity measurement



DOI: http://dx.doi.org/10.12928/telkomnika.v17i6.10698

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TELKOMNIKA Telecommunication, Computing, Electronics and Control
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