Dhanesh Ramachandram

Automatic white matter lesion segmentation using an adaptive outlier detection method

White matter (WM) lesions are diffuse WM abnormalities that appear as hyperintense (bright) regions in cranial magnetic resonance imaging (MRI). WM lesions are often observed in older populations and are important indicators of stroke, multiple sclerosis, dementia and other brain-related disorders. In this paper, a new automated method for WM lesions segmentation is presented. In the proposed method, the presence of WM lesions is detected as outliers in the intensity distribution of the fluid-attenuated inversion recovery (FLAIR) MR images using an adaptive outlier detection approach. Outliers are detected using a novel adaptive trimmed mean algorithm and box-whisker plot. In addition, pre- and postprocessing steps are implemented to reduce false positives attributed to MRI artifacts commonly observed in FLAIR sequences. The approach is validated using the cranial MRI sequences of 38 subjects. A significant correlation (R=0.9641, P value=3.12×10(-3)) is observed between the automated approach and manual segmentation by radiologist. The accuracy of the proposed approach was further validated by comparing the lesion volumes computed using the automated approach and lesions manually segmented by an expert radiologist. Finally, the proposed approach is compared against leading lesion segmentation algorithms using a benchmark dataset.

Dynamic fuzzy clustering using Harmony Search with application to image segmentation

In this paper, a new dynamic clustering approach based on the Harmony Search algorithm (HS) called DCHS is proposed. In this algorithm, the capability of standard HS is modified to automatically evolve the appropriate number of clusters as well as the locations of cluster centers. By incorporating the concept of variable length in each harmony memory vector, DCHS is able to encode variable numbers of candidate cluster centers at each iteration. The PBMF cluster validity index is used as an objective function to validate the clustering result obtained from each harmony memory vector. The proposed approach has been applied onto well known natural images and experimental results show that DCHS is able to find the appropriate number of clusters and locations of cluster centers. This approach has also been compared with other metaheuristic dynamic clustering techniques and has shown to be very promising.

A survey of the state of the art in learning the kernels

In recent years, the machine learning community has witnessed a tremendous growth in the development of kernel-based learning algorithms. However, the performance of this class of algorithms greatly depends on the choice of the kernel function. Kernel function implicitly represents the inner product between a pair of points of a dataset in a higher dimensional space. This inner product amounts to the similarity between points and provides a solid foundation for nonlinear analysis in kernel-based learning algorithms. The most important challenge in kernel-based learning is the selection of an appropriate kernel for a given dataset. To remedy this problem, algorithms to learn the kernel have recently been proposed. These methods formulate a learning algorithm that finds an optimal kernel for a given dataset. In this paper, we present an overview of these algorithms and provide a comparison of various approaches to find an optimal kernel. Furthermore, a list of pivotal issues that lead to efficient design of such algorithms will be presented.

An Optimization Algorithm Based on Harmony Search for RNA Secondary Structure Prediction

The determination of RNA molecules function relies heavily on its secondary structure. The current physical methods for RNA structure determination are time consuming and expensive. Hence, the methods of computational prediction of structure are the better alternatives. Various algorithms have been used for the RNA structure prediction, including dynamic programming and meta-heuristic algorithms. This chapter proposes the meta-heuristic harmony search algorithm (HSRNAFold) to find RNA secondary structure with minimum free energy and similarity to the native structure. HSRNAFold is compared to dynamic programming techniques: RNAFold and the benchmark, Mfold. The results show that HSRNAFold is comparable to dynamic programming in finding the minimum free energies in all RNA test sequences. The proposed method is efficient and promising in predicting the RNA secondary structure based on the minimum free energy.

Screening of Diabetic Retinopathy - Automatic Segmentation of Optic Disc in Colour Fundus Images

In this paper, a novel approach to automatically segment the optic disc contour using the center point of an optic disc candidate is proposed. The optic disc segmentation algorithm consists of 2 stages. The first stage involves the removal of blood vessels that obscure the optic disc. The blood vessel structures are detected using morphological operations. These detected structures are then removed by anisotropic diffusion smoothing. The second stage involves the detection of edge points belonging to the optic disc-contour. A number of one dimensional intensity profiles which pass through the center point of optic disc region are then obtained at multiple angles with fixed angular intervals. The modulus maxima of each intensity profile are identified as a contour point for the optic disc. Among these contour points, some of the outliers are removed by re-positioning to a new position which complies with optic discs shape using spline interpolation. Using these contour points, a coarse contour of the optic disc is constructed. Testing the approach on 23 colour fundus images demonstrates that the proposed algorithm is able to detect the optic disc contour to an accuracy of 92% to that drawn by a human expert

Harmony search-based cluster initialization for fuzzy c-means segmentation of MR images

We propose a new approach to tackle the well known fuzzy c-means (FCM) initialization problem. Our approach uses a metaheuristic search method called Harmony Search (HS) algorithm to produce near-optimal initial cluster centers for the FCM algorithm. We then demonstrate the effectiveness of our approach in a MRI segmentation problem. In order to dramatically reduce the computation time to find near-optimal cluster centers, we use an alternate representation of the search space. Our experiments indicate encouraging results in producing stable clustering for the given problem as compared to using an FCM with randomly initialized cluster centers.

Wavelet energy-guided level set-based active contour: A segmentation method to segment highly similar regions

This paper introduces an approach to perform segmentation of regions in computed tomography (CT) images that exhibit intra-region intensity variations and at the same time have similar intensity distributions with surrounding/adjacent regions. In this work, we adapt a feature computed from wavelet transform called wavelet energy to represent the region information. The wavelet energy is embedded into a level set model to formulate the segmentation model called wavelet energy-guided level set-based active contour (WELSAC). The WELSAC model is evaluated using several synthetic and CT images focusing on tumour cases, which contain regions demonstrating the characteristics of intra-region intensity variations and having high similarity in intensity distributions with the adjacent regions. The obtained results show that the proposed WELSAC model is able to segment regions of interest in close correspondence with the manual delineation provided by the medical experts and to provide a solution for tumour detection.

A Novel Image Segmentation Algorithm Based on Harmony Fuzzy Search Algorithm

Image segmentation is considered as one of the crucial steps in image analysis process and it is the most challenging task. Image segmentation can be modeled as a clustering problem. Therefore, clustering algorithms have been applied successfully in image segmentation problems. Fuzzy c-mean (FCM) algorithm is considered as one of the most popular clustering algorithm. Even that, FCM can generate a local optimal solution. In this paper we propose a novel Harmony Fuzzy Image Segmentation Algorithm (HFISA) which is based on Harmony Search (HS) algorithm. A model of HS which uses fuzzy memberships of image pixels to a predefined number of clusters as decision variables, rather than centroids of clusters, is implemented to achieve better image segmentation results and at the same time, avoid local optima problem. The proposed algorithm is applied onto six different types of images. The experiment results show the efficiency of the proposed algorithm compared to the fuzzy c-means algorithm.

Neural network-based robot visual positioning for intelligent assembly

A fundamental task in robotic assembly is the pick and place operation. Generally, this operation consists of three subtasks; guiding the robot to the target and positioning the manipulator in an appropriate pose, picking up the object and moving the object to a new location. In situations where the pose of the target may vary in the workspace, sensory feedback becomes indispensable to guide the robot to the object. Ideally, local image features must be clearly visible and un-occluded in multiple views of the object. In reality, this may not be always the case. Local image features are often are often rigidly constrained to a particular target and may require specialized feature localization algorithms. We present a visual positioning system that addresses feature extraction issues for a class of objects that have smooth or curved surfaces. In this work, the visual sensor consists of an arm mounted camera and a grid pattern projector that produces images with local surface description of the target. The projected pattern is always visible in the image and it is sensitive to variations in the object’s pose. A set of low-order geometric moments globally characterizes the observed pattern, eliminating the need for feature localization and overcoming the point correspondence problem. A neural network then learns the complex relationship between the robot’s pose displacements and the observed variations in the image features. After training, visual feedback guides the robot to the target from any arbitrary location in the workspace. Its applicability using a five degrees of freedom (DOF) industrial robot is demonstrated.

A Web-Based Framework for Distributed Medical Image Processing Using Image Markup Language (IML)

Image processing plays an important role in computer science, making complex image manipulation more feasible. It can be used either in a general manner or in specific domains such as for medical purposes. With the availability of the internet, image processing applications can be distributed to be available for different people, regardless of their geographical location. Although there are a handful of professional image processing solutions available, there is no well-designed framework that can be simply distributed and customized to be used in specific areas. Other than image processing tools, image storage and retrieval is another issue that needs to be addressed. This research proposes a general platform that can be used for implementing a distributed image processing framework. It provides solutions for storing and retrieving images, particularly large-size ones as well as an approach for executing image processing functions that are physically located anywhere within the distributed system.