Detection and discrimination of disease-related abnormalities based on learning normal cases

Detection of abnormalities from medical images is of key interest in developing computer-aided diagnostic tools. In this paper, we observe the key challenges for representation and feature extraction schemes to be met for detection of abnormalities by learning normal cases. We introduce an image representation, motivated by the effect of motion on perception of structures. This representation is based on a set of patterns called generalized moment patterns (GMP) generated via induced motion over regions of interest, for learning normal. The proposed GMP has been utilized to develop a scheme for addressing two well-known problems: lesion classification in mammograms and detection of macular edema in color fundus images. The strengths of this scheme are that it does not require any lesion-level segmentation and relies largely on normal images for training which is attractive for developing screening tools. The proposed scheme has been assessed on two public domain datasets, namely, MIAS and MESSIDOR. A comparison against the performance of state of the art methods indicates the proposed scheme to be superior.

► An image representation based on induced motion is proposed for abnormality detection.
► The spatio-temporal signatures are used for capturing the context of normal in medical images.
► Derived features on the representation are used for disease detection by learning normal cases.
► Proposed representation and features are validated on two well-known screening problems.