This paper presents extensions which improve the performance of the shape-based deformable active contour model presented earlier in [IEEE Conf. Comput. Vision Pattern Recog. 1 (2001) 463] for medical image segmentation. In contrast to that previous work, the segmentation framework that we present in this paper allows multiple shapes to be segmented simultaneously in a seamless fashion. To achi...

In this paper, we present an innovative topic segmentation system based on a new informative similarity measure that takes into account word co-occurrence in order to avoid the accessibility to existing linguistic resources such as electronic dictionaries or lexico-semantic databases such as thesauri or ontology. Topic Segmentation is the task of breaking documents into topically coherent multi...

This paper presents a semi-supervised Chinese word segmentation (CWS) approach that co-regularizes character-based and word-based models. Similarly to multi-view learning, the “segmentation agreements” between the two different types of view are used to overcome the scarcity of the label information on unlabeled data. The proposed approach trains a character-based and word-based model on labele...

This paper presents a novel segmentation method for identifying mass regions in mammograms. This work is a part of an on-going project whose aim is to build a Computer-Aided Diagnosis (CADx) system that classifies suspicious cancer masses in mammograms as benign or malignant. Segmentation of suspicious mass regions is an important pre-processing step to achieve high accuracy results, because th...

this paper presents, a hybrid method, low-resolution and high-resolution, for persian page segmentation. in the low-resolution page segmentation, a pyramidal image structure is constructed for multiscale analysis and segments document image to a set of regions. by high-resolution page segmentation, by connected components analysis, each region is segmented to homogeneous regions and identifying...

Texture analysis has been used extensively in the computer-assisted interpretation of digital imagery. A popular texture feature extraction approach is the grey level co-occurrence probability (GLCP) method. Most investigations consider the use of the GLCP texture features for classification purposes only, and do not address segmentation performance. Specifically, for segmentation, the pixels i...

We present a constraint adaptive image segmentation technique designed to achieve the combined purposes of color image coding/compression, indexing and content-based retrieval. An image is segmented into homogeneous squared regions of variable sizes such that it can be encoded very emciently [7]. From the segmented image, we derive an effective and efficient image content description feature te...

This paper presents a two-dimensional deformable model-based image segmentation method that integrates texture feature analysis into the model evolution process. Typically, the deformable models use edge and intensity-based features as the influencing image forces. Incorporation of the image texture information can increase the methods effectiveness and application possibilities. The algorithm ...

One of the most important tasks in image processing problem and machine vision is object recognition, and the success of many proposed methods relies on a suitable choice of algorithm for the segmentation of an image. This paper focuses on how to apply texture operators based on the concept of fractal dimension and cooccurence matrix, to the problem of object recognition and a new method based ...

optical coherence tomography (oct) is a powerful imaging modality used to image various aspects of biological tissues, such as structural information, blood flow, elastic parameters, change of polarization states, and molecular content [1]. in contrast to oct technology development which has been a field of active research since 1991, oct image segmentation has only been more fully explored dur...