Authors:
Amira Jouirou
1
;
Abir Baâzaoui
1
and
Walid Barhoumi
2
;
1
Affiliations:
1
Université de Tunis El Manar, Institut Supérieur d’Informatique, Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), LR16ES06 Laboratoire de Recherche en Informatique, Modélisation et Traitement de l’Information et de la Connaissance (LIMTIC), 2 Rue Abou Raihane Bayrouni, 2080 Ariana, Tunisia
;
2
Université de Carthage, Ecole Nationale d’Ingénieurs de Carthage, 45 Rue des Entrepreneurs, 2035 Tunis-Carthage, Tunisia
Keyword(s):
Content-based Mammogram Retrieval, Data-driven Distance Selection, Four Mammogram Views, Random Forest, Shared Information, Late Fusion.
Abstract:
Content-Based Mammogram Retrieval (CBMR) represents the most effective method for the breast cancer diagnosis, especially CBMR based on the fusion of different mammogram views. In this work, an efficient four-view CBMR method is proposed in order to further improve the mammogram retrieval performance. The proposed method consists in combining the retrieval results of the provided four views from the screening mammography, which are the Medio-Lateral Oblique (MLO) and the Cranio-Caudal (CC) views of the Left (LMLO and LCC) and the Right (RMLO and RCC) breasts. In order to personalize each query view in the final result, a classified mammogram dataset has been used to retrieve the relevant mammograms to the query. Indeed, the proposed method takes as input four query views corresponding to the four different views (LMLO, LCC, RMLO and RCC) and displays the most similar mammogram cases to each breast view using a dynamic data-driven distance selection and the shared information. In part
icular, we explore the use of random forest machine learning in order to predict the most appropriate similarity measure to each query view and the late fusion from the four view result-level, through the shared information concept, for the final retrieval. According to their clinical cases, the retrieved mammograms can be analyzed in order to help radiologists to make the right decision relatively to the four-view mammogram query. The reported experimental results from the challenging Digital Database for Screening Mammography (DDSM) dataset proved the effectiveness of the proposed four-view CBMR method.
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