Jatindra Kumar Dash

Rotation invariant textural feature extraction for image retrieval using eigen value analysis of intensity gradients and multi-resolution analysis

In this paper a novel rotation invariant multi-resolution based texture retrieval technique is proposed. The rotation invariance is achieved by aligning the direction of maximum variation of intensity gradient (defined as principal texture direction) along the reference axis. The principal direction is determined using eigen value analysis of gradient image. Wavelet transform based techniques are applied on the rotated image. The independent representation of textural energies along various directions enhances the retrieval performance over the existing rotation invariant wavelet based techniques which achieve rotation invariance by averaging the direction sensitive components. Extensive experiments on Brodatz database support this postulate.

Multiple classifier system using classification confidence for texture classification

This paper proposes a simple yet effective novel classifier fusion strategy for multi-class texture classification. The resulting classification framework is named as Classification Confidence-based Multiple Classifier Approach (CCMCA). The proposed training based scheme fuses the decisions of two base classifiers (those constitute the classifier ensemble) using their classification confidence to enhance the final classification accuracy. 4-fold cross validation approach is followed to perform experiments on four different texture databases those vary in terms of orientation, number of texture classes and complexity. Apart from its simplicity, the proposed CCMCA method shows better and consistent performance with lowest standard deviation as compared to fixed rule and simple trainable fusion techniques irrespective of the feature set used across all the databases used in the experiment. The performance gain of the proposed CCMCA method over other competing methods is found to be statistically significant.

Wavelet based features of circular scan lines for mammographic mass classification

Breast cancer is reported as the second most deadly cancer in the world on which public awareness has been increasing during the last few decades. Early detection can play an effective role in prevention and the most reliable detection technology is mammography. At the early stages of breast cancer, the clinical signs are very mild and vary in appearance, making diagnosis difficult even for specialists. Therefore, automatic reading of medical images becomes highly desirable. This paper aims to develop an automated system for mass classification in digital mammograms. Mini - MIAS database is used to obtain mammogram images. A novel approach for feature extraction is proposed which exploits the wavelet features of radial and circular scan lines drawn over the region of interest (ROI). The discriminating ability of these features are evaluated using three classifiers such as Neural Network (Scaled conjugate back propagation), Bayesian and Support Vector Machine (SVM). The experimental results show that SVM outperforms with an accuracy of 85.96%.

Content-based image retrieval for interstitial lung diseases

Finding similar images or reference is one way to assist radiologist during daily clinical practice for differential diagnosis of Interstitial Lung Diseases (ILDs). Content Based Image Retrieval (CBIR) system could exploit the wealth of HRCT data stored in the archive by finding similar images or reference to assist radiologists during daily clinical practice. We have designed a special purpose CBIR system (Med-IR) for Interstitial Lung Diseases (ILDs), where the user can provide one interstitial disease pattern as input and the system will retrieve few most similar patterns available in the database. Three different feature extraction techniques are implemented. A graphical interface has been developed to give a query image and to display the retrieved images. The retrieval performances of three rotation invariant texture feature sets derived using Discrete Wavelet Transform (DWT), Dual Tree Complex Wavelet Transform (DT-CWT) and DT-CWT combined with Dual Tree Rotated Complex Wavelet Frame (DT-RCWF) are compared in terms of average precision and recall for ILDs pattern. The dataset used for evaluation contains 64 images representing four ILDs pattern such as consolidation, nodular, emphysema, ground glass and normal. It is observed that feature obtained using DT-CWT and DT-CWT combined with DT-RCWT out performs the features obtained using DWT techniques.

Content-based image retrieval for interstitial lung diseases using classification confidence

Content Based Image Retrieval (CBIR) system could exploit the wealth of High-Resolution Computed Tomography (HRCT) data stored in the archive by finding similar images to assist radiologists for self learning and differential diagnosis of Interstitial Lung Diseases (ILDs). HRCT findings of ILDs are classified into several categories (e.g. consolidation, emphysema, ground glass, nodular etc.) based on their texture like appearances. Therefore, analysis of ILDs is considered as a texture analysis problem. Many approaches have been proposed for CBIR of lung images using texture as primitive visual content. This paper presents a new approach to CBIR for ILDs. The proposed approach makes use of a trained neural network (NN) to find the output class label of query image. The degree of confidence of the NN classifier is analyzed using Naive Bayes classifier that dynamically takes a decision on the size of the search space to be used for retrieval. The proposed approach is compared with three simple distance based and one classifier based texture retrieval approaches. Experimental results show that the proposed technique achieved highest average percentage precision of 92.60% with lowest standard deviation of 20.82%.

Content-based image retrieval using fuzzy class membership and rules based on classifier confidence

Content representation for images with well-defined inter-class boundaries in the feature space remains to be a difficult task. Simple distance-based retrieval (SDR) approaches those operate on the feature space for content-based image retrieval (CBIR) are, therefore claimed to be inefficient by many researchers. Different CBIR approaches have been proposed to surmount the drawbacks of SDR scheme. This study proposes a novel image retrieval scheme. In this scheme, effort is taken to reduce the overall search time of the recently proposed approach called ‘class membership-based retrieval’ (CMR). The proposed method identifies the confidence in the classification and limits the search to single output class and therefore, reduces the overall search time by 21.76% as compared to CMR. Quantitative methods are proposed to select various parameters used in the algorithm which were computed empirically in the case of earlier approach CMR. The computed parameters are validated using experimental results. The consistent behaviours of the proposed method and earlier methods used in the experiment are demonstrated using different feature sets and distance metrics. While the method can be used as a general purpose image retrieval system, experiment is performed on four texture databases wit different complexities in terms of size, number of texture classes and orientation.

Similarity learning for texture image retrieval using multiple classifier system

Multiple Classifier System has found its applications in many areas such as handwriting recognition, speaker recognition, medical diagnosis, fingerprint recognition, personal identification and others. However, there have been rare attempts to develop content-based image retrieval (CBIR) system that uses multiple classifiers to learn visual similarity. Texture as a primitive visual content is often used in many important applications (viz. Medical image analysis and medical CBIR system). In this paper, a texture image retrieval system is developed that learns the visual similarity in terms of class membership using multiple classifiers. The way proposed approach combines the decisions of multiple classifiers to obtain final class memberships of query for each of the output classes is also a novel concept. A modified distance that is weighted with the membership values obtained through similarity learning is used for ranking. Three different algorithms are proposed for the retrieval of images against a query image displaying the strength of multiple classifier approach, class membership score and their interplay to achieve the objective defined in terms of simplicity, retrieval effectiveness and speed. The proposed methods based on multiple classifiers achieve higher retrieval accuracy with lower standard deviation compared to all the competing methods irrespective of the texture database and feature set used. The multiple classifier retrieval schemes proposed here is tested for texture image retrieval. However, these can be used for any other challenging retrieval problems.

Segmentation of interstitial lung disease patterns in HRCT images

Automated segmentation of pathological bearing region is the first step towards the development of lung CAD. Most of the work reported in the literature related to automated analysis of lung tissue aims towards classification of fixed sized block into one of the classes. This block level classification of lung tissues in the image never results in accurate or smooth boundaries between different regions. In this work, effort is taken to investigate the performance of three automated image segmentation algorithms those results in smooth boundaries among lung tissue patterns commonly encountered in HRCT images of the thorax. A public database that consists of HRCT images taken from patients affected with Interstitial Lung Diseases (ILDs) is used for the evaluation. The algorithms considered are Markov Random Field (MRF), Gaussian Mixture Model (GMM) and Mean Shift (MS). 2-fold cross validation approach is followed for the selection of the best parameter value for individual algorithm as well as to evaluate the performance of all the algorithms. Mean shift algorithm is observed as the best performer in terms of Jaccard Index, Modified Hausdorff Distance, accuracy, Dice Similarity Coefficient and execution speed.

Differentiation of several interstitial lung disease patterns in HRCT images using support vector machine: role of databases on performance.

Interstitial lung disease (ILD) is complicated group of pulmonary disorders. High Resolution Computed Tomography (HRCT) considered to be best imaging technique for analysis of different pulmonary disorders. HRCT findings can be categorised in several patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Nodular, Normal etc. based on their texture like appearance. Clinician often find it difficult to diagnosis these pattern because of their complex nature. In such scenario computer-aided diagnosis system could help clinician to identify patterns. Several approaches had been proposed for classification of ILD patterns. This includes computation of textural feature and training /testing of classifier such as artificial neural network (ANN), support vector machine (SVM) etc. In this paper, wavelet features are calculated from two different ILD database, publically available MedGIFT ILD database and private ILD database, followed by performance evaluation of ANN and SVM classifiers in terms of average accuracy. It is found that average classification accuracy by SVM is greater than ANN where trained and tested on same database. Investigation continued further to test variation in accuracy of classifier when training and testing is performed with alternate database and training and testing of classifier with database formed by merging samples from same class from two individual databases. The average classification accuracy drops when two independent databases used for training and testing respectively. There is significant improvement in average accuracy when classifiers are trained and tested with merged database. It infers dependency of classification accuracy on training data. It is observed that SVM outperforms ANN when same database is used for training and testing.

Wavelet based rotation invariant texture feature for lung tissue classification and retrieval

This paper evaluates the performance of recently proposed rotation invariant texture feature extraction method for the classi¯cation and retrieval of lung tissues a®ected with Interstitial Lung Diseases (ILDs). The method makes use of principle texture direction as the reference direction and extracts texture features using Discrete Wavelet Transform (DWT). A private database containing high resolution computed tomography (HRCT) images belonging to ¯ve category of lung tissue is used for the experiment. The experimental result shows that the texture appearances of lung tissues are anisotropic in nature and hence rotation invariant features achieve better retrieval as well as classi¯cation accuracy.