Content based image retrieval for maxillofacial lesions

This paper presents a novel approach for medical content based image retrieval, searching for images which are pathologically similar to a given example and demonstrates itsperformance on a dataset containing maxillofacial lesions. As distributed databases at hospitals and CT scanning centers are connected together through Picture Archiving and Communication Systems (PACS), a content based image retrieval system is needed to help radiologists in medical diagnosis. In our proposed framework, a feature vector is extracted for each image and SIFT sparse codes are employed in this step. After feature extraction based on sparse coding and maximum pooling, we have utilized different similarity measures such as Euclidean norm, Manhattan distance and SVM classifier to choose most relevant images to the query image. We have evaluated our proposed framework on a dataset containing 2023 images belonging to 5 different categories: cleft, impaction, fracture, maxillary sinus cyst and healthy. Classification rate of 97.3% andprecision versus recall curve indicate the effectiveness of sparse coding in content-based medical image retrieval

Keywords:

Cone beam CT , content based image retrieval , maxillofacial lesions , sparse representation