Integration of feature distributions for colour texture segmentation

This paper proposes a new framework for colour texture segmentation and determines the contribution of colour and texture. The distributions of colour and texture features provides the discrimination between different colour textured regions in an image. The proposed method was tested using different mosaic and natural images. From the results, it is evident that the incorporation of colour information enhanced the colour texture segmentation and the developed framework is effective

A generic framework for colour texture segmentation

This thesis proposes a novel method to combine the colour and the texture for colour texture segmentation. The objective of this research work is to derive a framework for colour texture segmentation and to determine the contribution of colour in colour texture analysis. The colour texture processing is based on the feature extraction from colour-textured images. The texture features were obtained from the luminance plane along with the colour features from the chrominance planes. Based on the above mentioned approach, a method was developed for colour texture segmentation. The proposed method unifies colour and texture features to solve the colour texture segmentation problem. Two of the grey scale texture analysis techniques, Local Binary Pattern (LBP) and Discrete Cosine Transform (DCT) based filter approach were extended to colour images. An unsupervised fc-means clustering was used to cluster pixels in the chrominance planes. Non-parametric test was used to test the similarity between colour texture regions. An unsupervised texture segmentation method was followed to obtain the segmented image. The evaluation of the segmentation was based on the ROC curves. A quantitative estimation of colour and texture performance in segmentation was presented. The use of different colour spaces was also investigated in this study. The proposed method was tested using different mosaic and natural images obtained from VisTex and other predominant image database used in computer vision. The applications for the proposed colour texture segmentation method are, Irish Script On Screen (ISOS) images for the segmentation of the colour textured regions in the document, skin cancer images to identify the diseased area, and Sediment Profile Imagery (SPI) to segment underwater images. The inclusion of colour and texture as distributions of regions provided a good discrimination of the colour and the texture. The results indicated that the incorporation of colour information enhanced the texture analysis techniques and the methodology proved effective and efficient

Segmentation of skin cancer images

The aim of this study is to provide an efficient way to segment the skin cancer images. A novel method isproposed that combines colour and texture for the segmentation of skin lesions from unaffected skin region in an image. The distributions of colour and texture features provide a good discrimination of skin lesions. The evaluation of the proposed method was based on the comparison with the Live Wire segmentation results. The segmentation results are evaluated quantitatively by means of a comparative experiment on a set of skin cancer images. The results indicate that the developed methodology proved effective and efficient for the skin cancer image segmentatio

Non-invasive Glaucoma Screening Using Ocular Thermal Image Classification

Ocular thermography is an important, emerging modality in the diagnosis and management of diseases related to eye. It is a non-invasive procedure to evaluate the presence of eye diseases and monitor the response to treatments. In this paper, we propose and evaluate a system designed using infrared thermal image processing that detects glaucoma. Euclidean distance based segmentation technique is used to threshold the IR image to obtain the region of interest, where the manifestation of glaucoma is predominant. Features are extracted using statistical moments from the temperature mapped IR image and Gray Level Co-Occurrence Matrix of the IR image. Two significant attributes, namely the homogeneity and area of region of interest are the inputs to a Support Vector Machine classifier to classify a given input ocular thermal image as a normal or diseased image. In our simulation study, one hundred ocular thermal images with even number of normal and diseased subjects were analysed. The classifier has achieved a maximum accuracy of 96% when homogeneity and area of region of interest are used as attributes, indicating the potential use of proposed method for screening patients even at early stages of glaucoma

Quality grading of painted slates using texture analysis

This paper details the development of an automated vision-based solution for identification of paint and substrate defects on painted slates. The developed vision system consists of two major components. The first component of the system addresses issues including the mechanical implementation and interfacing the inspection system with the sensing and optical equipment. The second component involves the development of an image processing algorithm that is able to identify the visual defects present on the slate surface. The process of imaging the slate proved to be very challenging as the slate surface is darkly coloured and presents depth non-uniformities. Hence, a key issue for this inspection system was to devise an adequate illumination system that was able to accommodate challenges including the slates’ surface depth non-uniformities and vibrations generated by the conveying system. The visual defects are detected using a novel texture analysis solution where the greyscale (tonal characteristics) and texture information are embedded in a composite model. The developed inspection system was tested for robustness and experimental results are presented

Integration of colour and texture distributions for skin cancer image segmentation

This study presents an efficient way to segment the skin cancer images. A novel method is proposed that combines colour and texture for the segmentation of skin lesions from unaffected skin region in an image. The distributions of colour and texture features provide a platform for the discrimination of skin lesions. The segmentation results are evaluated quantitatively by means of a comparative experiment on a set of skin cancer images. The evaluation of the proposed method is based on the comparison with Live Wire segmentation technique. The results indicate that the proposed methodology proved effective and efficient for the skin cancer image segmentation