Optimal query-based relevance feedback in medical image retrieval using score fusion-based classification

An Efficient Content-Based Image Retrieval System for the Diagnosis of Lung Diseases

The main problem in content-based image retrieval (CBIR) systems is the semantic gap which needs to be reduced for efficient retrieval. The common imaging signs (CISs) which appear in the patient's lung CT scan play a significant role in the identification of cancerous lung nodules and many other lung diseases. In this paper, we propose a new combination of descriptors for the effective retrieval of these imaging signs. First, we construct a feature database by combining local ternary pattern (LTP), local phase quantization (LPQ), and discrete wavelet transform. Next, joint mutual information (JMI)-based feature selection is deployed to reduce the redundancy and to select an optimal feature set for CISs retrieval. To this end, similarity measurement is performed by combining visual and semantic information in equal proportion to construct a balanced graph and the shortest path is computed for learning contextual similarity to obtain final similarity between each query and database image. The proposed system is evaluated on a publicly available database of lung CT imaging signs (LISS), and results are retrieved based on visual feature similarity comparison and graph-based similarity comparison. The proposed system achieves a mean average precision (MAP) of 60% and 0.48 AUC of precision-recall (P-R) graph using only visual features similarity comparison. These results further improve on graph-based similarity measure with a MAP of 70% and 0.58 AUC which shows the superiority of our proposed scheme.

A novel retina-based human identification algorithm based on geometrical shape features using a hierarchical matching structure

BACKGROUND AND OBJECTIVES

Retinal image is one of the most secure biometrics which is widely used in human identification application. This paper represents a rotation and translation-invariant human identification algorithm based on a new definition of geometrical shape features of the retinal image using a hierarchical matching structure.

METHODS

In this algorithm, the retinal images are represented by regions which are surrounded by blood vessels that are named Surrounded Regions (SRs). A perfect set of region-based and boundary-based features are defined on the SRs. In the boundary-based features, by defining corner points of the SR, novel features such as angle of SR corner, centroid distance and weighted corner angle are defined which they can describe well the variation rate of boundary and geometry of the SR. To match the SR of a query with enrolled SR in database, the extracted features in a hierarchical structure from simpler features through more complex features are applied to filter the enrolled SRs in the database for search space reduction. At last, the matched candidate SRs with the query SRs determine the identification or rejection of query image by proposed decision making scenario. In this scenario, the identification is carried out when at least two SRs of the query are matched with two SRs of an individual in the database.

RESULTS

The proposed algorithm is evaluated on STARE and DRIVE retinal image databases in six different experiments and is achieved an accuracy rate of 100% and an average processing time of 3.216sec and 3.225sec, respectively. The reported results demonstrate the efficiency of our proposed algorithm in the eye-movement condition.

CONCLUSION

In our work, by defining the SR-based features and employing a hierarchical matching structure, the computational complexity of matching step is reduced and also the identification performance is improved. Moreover, the proposed algorithm overcomes the problem of natural movements of the head and eye during the capturing process.

Interactive radiographic image retrieval system

BACKGROUND AND OBJECTIVE

Content based medical image retrieval (CBMIR) systems enable fast diagnosis through quantitative assessment of the visual information and is an active research topic over the past few decades. Most of the state-of-the-art CBMIR systems suffer from various problems: computationally expensive due to the usage of high dimensional feature vectors and complex classifier/clustering schemes. Inability to properly handle the "semantic gap" and the high intra-class versus inter-class variability problem of the medical image database (like radiographic image database). This yields an exigent demand for developing highly effective and computationally efficient retrieval system.

METHODS

We propose a novel interactive two-stage CBMIR system for diverse collection of medical radiographic images. Initially, Pulse Coupled Neural Network based shape features are used to find out the most probable (similar) image classes using a novel "similarity positional score" mechanism. This is followed by retrieval using Non-subsampled Contourlet Transform based texture features considering only the images of the pre-identified classes. Maximal information compression index is used for unsupervised feature selection to achieve better results. To reduce the semantic gap problem, the proposed system uses a novel fuzzy index based relevance feedback mechanism by incorporating subjectivity of human perception in an analytic manner.

RESULTS

Extensive experiments were carried out to evaluate the effectiveness of the proposed CBMIR system on a subset of Image Retrieval in Medical Applications (IRMA)-2009 database consisting of 10,902 labeled radiographic images of 57 different modalities. We obtained overall average precision of around 98% after only 2-3 iterations of relevance feedback mechanism. We assessed the results by comparisons with some of the state-of-the-art CBMIR systems for radiographic images.

CONCLUSIONS

Unlike most of the existing CBMIR systems, in the proposed two-stage hierarchical framework, main importance is given on constructing efficient and compact feature vector representation, search-space reduction and handling the "semantic gap" problem effectively, without compromising the retrieval performance. Experimental results and comparisons show that the proposed system performs efficiently in the radiographic medical image retrieval field.