Authors are requested to select key words from the following list. You may use plurals and adjectives for combinations like absolute pupillary response, centrifugal optic nerve, inter blobs, etc. If key words are needed which do not appear in this list, please underline them. For animal classification the Latin nomenclature should be used. p-mechanism A-scan a-wave aberration absolute absorptio...

Today, a wide range of different image segmentation techniques can be found in medical imaging. Image segmentation is an important part of image processing, which often has a large impact on quantitative image analysis results. These allow the radiologist to obtain two and three-dimensional view of the internal structures of the human body, it has a large application domain for redundant clinic...

In the paper, we proposed a model called Adaptive Threshold Level Set Without Edge that is applied on the segmentation of GM in brain MR images. Threshold to find the boundary of GM is automatically obtained by fuzzy c mean algorithm. A similarity index (SI) is used for quantitative evaluation of the segmentation results. By testing 134 planar MR brain images and comparing to the gold standard ...

Image segmentation plays a major role in quantitative image analysis and computer aided detection (CAD) and diagnosis (CADx) for clinical applications. Conventional segmentation assigns a single label to each voxel, neglecting the partial volume (PV) effect. This work presents an EM (Expectation Maximization) framework for segmentation of tissue mixture in each voxel. Image data and tissue mixt...

A new method of word segmentation for document images is presented. The method uses the bounding box regions to enclose the letters (characters) of the words and then the resulting letter spaces are progressively filled to merge the character bounding boxes to get the word bounding boxes. The method holds good for inclined and irregularly distributed words. The proposed method completely avoids...

A major problem in noisy image processing is the effective segmentation of its components. In this paper, we are proposing a K Medoid clustering algorithm for noisy image segmentation, which is able to segment all types of noisy images efficiently. As the presented clustering algorithm selects the centroids randomly hence it is less sensitive , to any type of noise as compare to other clusterin...

In this paper we propose segmentation approach based on Vector Quantization technique. Here we have used Kekre’s fast codebook generation algorithm for segmenting low-altitude aerial image. This is used as a preprocessing step to form segmented homogeneous regions. Further to merge adjacent regions color similarity and volume difference criteria is used. Experiments performed with real aerial i...

Segmentation of adjoining objects in a noisy image is a challenging task in image processing. Natural images often get corrupted by noise during acquisition and transmission. Segmentation of these noisy images does not provide desired results, hence de-noising is required. In this paper, we tried to address a very effective technique called Wavelet thresholding for denoising, as it can arrest t...

In this paper, we propose to rely on a recent image representation model, namely the α-tree, to achieve efficient segmentation of images and videos. The α-tree is a multiscale representation of an image, based on its quasi-flat zones. An in-depth study of this tree reveals some interesting features of image pixels and regions. These features are then used in the design of both automatic and int...

Segmentation plays a vital role in determining the tumor in brain MR Images. The analysis is done using multifractional Brownian motion (mBm) to devise the tumor in brain MR images. The spatially varying feature is extracted using mBm and corresponding algorithm. Then segmentation is carried out based on multifractal features. An algorithm for segmentation is proposed by modifying the well-know...