A Sub-block Based Image Retrieval Using Modified Integrated Region Matching

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding followed by morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. The colour and texture feature vectors is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching (IRM) algorithm is used for finding the minimum distance between the sub-blocks of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.Comment: 7 page

CBIR Using Local and Global Properties of Image Sub-blocks

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation and finding the corner density in each partition. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). Euclidean distance measure is used for computing the distance between the features of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods

Improving Recurrence Prediction Accuracy of Ovarian Cancer Using Multi-phase Feature Selection Methodology

Ovarian cancer stands in the sixth position among the most commonly occurring cancers in the world. Because of the high rate of recurrence, this gynecological malignancy seems to be a vital reason behind cancer-related death among women as tumor recurrence stands as an obstacle in ovarian cancer treatment. It is crucial to find those recurrence causing factors in order to plan suitable therapies with high prognostic results. Hence, in this work, a multistage feature selection methodology is proposed to identify key MiRNAs and clinical features for improving the accuracy of ovarian cancer recurrence prediction. MiRNA expression profiles of ovarian cancer patients and their corresponding clinical data were downloaded from the TCGA cancer repository. From 588 MiRNAs, 6 key MiRNAs were selected using the Inheritable Bi-objective Combinatorial Genetic Algorithm (IBCGA) followed by factor analysis. The biological importance of the resultant MiRNAs in cancer and cellular pathways were studied using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Further, recurrence prediction was performed using the obtained MiRNA expression profiles and clinical factors, chosen using correlation analysis. The proposed approach using the selected features yielded a prediction accuracy of 91.86% using the XGBoost classifier while the same without feature selection was 76.59%. Compared to previous similar works, this model provides a better result in terms of accuracy and reveals influential MiRNAs in ovarian cancer

Image retrieval based on effective feature extraction and diffusion process

Feature extraction and its matching are two critical tasks in image retrieval. This paper presents a new methodology for content-based image retrieval by integrating three features, and then optimizing feature metric by diffusion process. To boost the discriminative power, the color histogram, local directional pattern, and dense SIFT features based on bag of features (BoF) are selected. Then diffusion process is applied to seek a global optimization for image matching based on fused multi-features. The diffusion process can capture the intrinsic manifold structure on a dataset, and thus enhance the overall retrieval performance significantly. Finally, a new search strategy is explored to make the diffusion process work even better when the number of retrieval images is small. In order to validate our proposed approach, four benchmark databases are used, and the results of experiments show that the proposed approach outperforms all other existing approaches