Case based reasoning (CBR) with image retrieval can be used to implement a clinical decision support system for supporting diagnosis of space occupying lesions . We present a case based image retrieval (CBIR) system to retrieve images with lesion similar to the input test image. Here we consider only glioblasoma and lung cancer lesions. The lung cancer lesions can be either nodules or cysts. A feature database has been created and the processing of a query is conducted in real time. By using bag of visual words (BOVW), histogram of features is compared with the codebook to retrieve similar images. The experiments performed at various levels retrieved relevant and similar images of lesion images with a mean average precision of 0.85. The system presented is expected aid and improve the effectiveness of diagnosis performed by radiologist.
Advances in medical imaging technologies have led to the generation of large databases with high-resolution image volumes. To retrieve images with pathology similar to the one under examination, we propose a content- based image retrieval framework (CBIR) for medical image retrieval using deep Convolutional Neural Network (CNN). We present retrieval results for medical images using a pre-trained neural network, ResNet-18. A multi- modality dataset that contains twenty-three classes and four modalities including (Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Mammogram (MG), and Positron Emission Tomograph (PET)) are used for demonstrating our method. We obtain an average classification accuracy of 92% and the mean average precision of 0.90 for retrieval. The proposed method can assist in clinical diagnosis and training radiologist.