Ashis Kumar Dhara

Performance metrics for image contrast

In this paper, contrast level of the images are quantified by the two proposed metrics. These metrics are Histogram Flatness Measure (HFM) and Histogram Spread (HS). Computation of these metrics is based on the shape of the histogram. Extensive simulation results reveal that HS is more meaningful than HFM. Low contrast images have low HS value, while high contrast images have higher value of HS. Thus HS metric can be used to distinguish between the images having different contrast level. Accuracy of the metric is also verified for natural and medical images. This metric has broad applications in image retrieval, image database management, visualization, rendering and image classification.

A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images

Classification of malignant and benign pulmonary nodules is important for further treatment plan. The present work focuses on the classification of benign and malignant pulmonary nodules using support vector machine. The pulmonary nodules are segmented using a semi-automated technique, which requires only a seed point from the end user. Several shape-based, margin-based, and texture-based features are computed to represent the pulmonary nodules. A set of relevant features is determined for the efficient representation of nodules in the feature space. The proposed classification scheme is validated on a data set of 891 nodules of Lung Image Database Consortium and Image Database Resource Initiative public database. The proposed classification scheme is evaluated for three configurations such as configuration 1 (composite rank of malignancy “1” and “2” as benign and “4” and “5” as malignant), configuration 2 (composite rank of malignancy “1”,“2”, and “3” as benign and “4” and “5” as malignant), and configuration 3 (composite rank of malignancy “1” and “2” as benign and “3”,“4” and “5” as malignant). The performance of the classification is evaluated in terms of area (Az) under the receiver operating characteristic curve. The Az achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 0.9505, 0.8822, and 0.8488, respectively. The proposed method outperforms the most recent technique, which depends on the manual segmentation of pulmonary nodules by a trained radiologist.

Computer-aided Detection and Analysis of Pulmonary Nodule from CT Images: A Survey

Abstract The pulmonary nodules are the most common manifestation of lung cancer. Computer tomography (CT) images provide a very good technology for pulmonary nodule detection because of the thin-slice chest scan. This large number of thin-slice leads to the development of the computer-aided detection and analysis system for pulmonary nodule to assist radiologists. This paper presents a review of the literature from 1998-2010 on automated detection of pulmonary nodule including methods of false positive reduction, nodule characterization, and volumetric analysis of segmented nodule. In addition, research trends and challenges are identified and directions for future research are discussed.

Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer

Visual information of similar nodules could assist the budding radiologists in self-learning. This paper presents a content-based image retrieval (CBIR) system for pulmonary nodules, observed in lung CT images. The reported CBIR systems of pulmonary nodules cannot be put into practice as radiologists need to draw the boundary of nodules during query formation and feature database creation. In the proposed retrieval system, the pulmonary nodules are segmented using a semi-automated technique, which requires a seed point on the nodule from the end-user. The involvement of radiologists in feature database creation is also reduced, as only a seed point is expected from radiologists instead of manual delineation of the boundary of the nodules. The performance of the retrieval system depends on the accuracy of the segmentation technique. Several 3D features are explored to improve the performance of the proposed retrieval system. A set of relevant shape and texture features are considered for efficient representation of the nodules in the feature space. The proposed CBIR system is evaluated for three configurations such as configuration-1 (composite rank of malignancy “1”,“2” as benign and “4”,“5” as malignant), configuration-2 (composite rank of malignancy “1”,“2”, “3” as benign and “4”,“5” as malignant), and configuration-3 (composite rank of malignancy “1”,“2” as benign and “3”,“4”,“5” as malignant). Considering top 5 retrieved nodules and Euclidean distance metric, the precision achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 82.14, 75.91, and 74.27 %, respectively. The performance of the proposed CBIR system is close to the most recent technique, which is dependent on radiologists for manual segmentation of nodules. A computer-aided diagnosis (CAD) system is also developed based on CBIR paradigm. Performance of the proposed CBIR-based CAD system is close to performance of the CAD system using support vector machine.

Differential geometry-based techniques for characterization of boundary roughness of pulmonary nodules in CT images

PurposeBoundary roughness of a pulmonary nodule is an important indication of its malignancy. The irregularity of the shape of a nodule is represented in terms of a few diagnostic characteristics such as spiculation, lobulation, and sphericity. Quantitative characterization of these diagnostic characteristics is essential for designing a content-based image retrieval system and computer-aided system for diagnosis of lung cancer.MethodsThis paper presents differential geometry-based techniques for computation of spiculation, lobulation, and sphericity using the binary mask of the segmented nodule. These shape features are computed in 3D considering complete nodule.ResultsThe performance of the proposed and competing methods is evaluated in terms of the precision, mean similarity, and normalized discounted cumulative gain on 891 nodules of Lung Image Database Consortium and Image Database Resource Initiative. The proposed methods are comparable to or better than gold standard technique. The reproducibility of proposed feature extraction techniques is evaluated using RIDER coffee break data set. The mean and standard deviation of the percent change of spiculation, lobulation, and sphericity are

3D texture analysis of solitary pulmonary nodules using co-occurrence matrix from volumetric lung CT images

1.66\pm 2.36

Automated lung field segmentation in CT images using mean shift clustering and geometrical features

1.66±2.36,