MARKER CONTROLLED WATERSHED SEGMENTATION USING BIT-PLANE SLICING

Image segmentation is the basis for computer vision and object recognition. Watershed transform is one of the common methods used for region based segmentation. The previous watershed methods results in over segmentation. In this paper we present a novel method for efficient image segmentation by using bit-plane slicing and marker-controlled watershed. Bit-Plane slicing method produces the sliced image by taking the most significant bit of the image as the input to the bit-plane slicing algorithm. The output of the Bit-Plane slicing algorithm is used to produce the gradient image .The watershed segmentation algorithm is applied to the average of the marker image and the gradient image so as to get efficient segmentation result. Experimental results, shows that the proposed method reduces the memory consumption and computation

Watershed segmentation with boundary curvature ratio based merging criterion

This paper proposes to incorporate boundary curvature ratio, region homogeneity and boundary smoothness into a single new merging criterion to improve the oversegmentation of marker-controlled watershed segmentation algorithm. The result is a more refined segmentation result with smooth boundaries and regular shapes. To pursue a final segmentation result with higher inter-variance and lower intra-variance, an optimal number of segments could be self-determined by a proposed formula. Experimental results are presented to demonstrate the merits of this method.postprintThe 9th IASTED International Conference on Signal and Image Processing (SIP 2007), Honolulu, HI., 20-22 August 2007. In Proceedings of SIP, 2007, p. 7-1

Effects of Noninhibitory Serpin Maspin on the Actin Cytoskeleton: A Quantitative Image Modeling Approach

Recent developments in quantitative image analysis allow us to interrogate confocal microscopy images to answer biological questions. Clumped and layered cell nuclei and cytoplasm in confocal images challenges the ability to identify subcellular compartments. To date, there is no perfect image analysis method to identify cytoskeletal changes in confocal images. Here, we present a multidisciplinary study where an image analysis model was developed to allow quantitative measurements of changes in the cytoskeleton of cells with different maspin exposure. Maspin, a noninhibitory serpin influences cell migration, adhesion, invasion, proliferation, and apoptosis in ways that are consistent with its identification as a tumor metastasis suppressor. Using different cell types, we tested the hypothesis that reduction in cell migration by maspin would be reflected in the architecture of the actin cytoskeleton. A hybrid marker-controlled watershed segmentation technique was used to segment the nuclei, cytoplasm, and ruffling regions before measuring cytoskeletal changes. This was informed by immunohistochemical staining of cells transfected stably or transiently with maspin proteins, or with added bioactive peptides or protein. Image analysis results showed that the effects of maspin were mirrored by effects on cell architecture, in a way that could be described quantitatively

Segmentation and Extraction of Individual Leaves from Plant Images for Species Classification

Plant species classification through the examination of images of plant leaves requires as input an image of a single leaf with no stems or other non-leaf objects. Images of plants, however, usually include more than one leaf, stems, branches, flowers, and other non-leaf objects. For such images each individual leaf needs to be extracted into a unique sub-image, and these sub-images must be cleaned to remove all non-leaf objects. A target leaf could then be selected from the group of sub-images to be provided as the input to the plant species classification program. As a part of the research on this thesis, an algorithm was developed to automate the tasks of detecting and extracting leaf sub-images from plant images and to clean the leaf sub-images by removing all non-leaf objects. To implement the algorithm, software was developed in Java. The proposed algorithm produced at least one perfect leaf result in 18 of the 21 (86%) plant images used in this research, while the remaining three (14%) plant images produced acceptable leaves

Image Processing for Medical Image Analysis: A Review

Image processing techniques are used widely in medical areas for improving the image in earlier detection and treatment stages, it is very important to discover the abnormality issues in given images, specially in various cancer, tumours such as lung cancer, breast cancer, etc. Image quality and accuracy is the main factors of this work, image quality improvement and assessment are depending on the enhancement stage where pre-processing techniques is used. The principal objectives of this course are to provide basic introduction and techniques for medical image processing and to promote for further study and research in medical image processing

Image Segmentation and Classification for Medical Image Processing

Segmentation and labeling remains the weakest step in many medical vision applications. This paper illustrates an approach based on watershed transform which are designed to solve typical problems encountered in various applications, and which are controllable through adaptation of their parameters. Two of these modules are presented: the lung cancer detection, a method for the segmentation of cancer regions from CT images, a watershed algorithm for image segmentation and brain tumor detection from MRI images. Various GLCM features along with some statistical features are used for classification using Neural network and Support Vector Machine (SVM). We describe the principles of the algorithms and illustrate their generic properties by discussing the results of both applications in 2D MRI images of Brain tumor and CT images of lung cancer