Sharif Digital Repository

Optical Character Recognition (OCR) softwares are widely used in the office automation systems. One of the first steps in the recognition of the documents is to segment the input image. Various methods have been offered for the English language. For the Persian/Arabic Language, however, no complete method has been found yet. In this paper we present a new page segmentation method for Persian/Arabic printed texts. This method has been inspired by the effect of the spreading of ink on paper. One of the most important characteristics of this method is its non-sensitivity to rotation  

Processing time and segmentation quality are two main factors in evaluation of image segmentation methods. Oversegmentation is one of the most challenging problems that significantly degrade the segmentation quality. This paper presents a framework for decreasing the oversegmentation rate and improving the processing time. Significant variations in both color and texture spaces are the main reasons that lead to oversegmentation. We exploit Affine Invariant Region Detectors to mark regions with high variations in both color and texture spaces. The results are then utilized to reduce the oversegmentation rate of image segmentation algorithms. The performance of the proposed framework is... 

Image segmentation with clustering approach is widely used in biomedical application. Fuzzy c-means (FCM) clustering is able to preserve the information between tissues in image, but not taking spatial information into account, makes segmentation results of the standard FCM sensitive to noise. To overcome the above shortcoming, a modified FCM algorithm for MRI brain image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster by smoothing it by Total Variation (TV) denoising. The proposed algorithm is evaluated with accuracy index in... 

This paper presents a new segmentation method for color images. It relies on soft and hard segmentation processes. In the soft segmentation process, a cellular learning automata analyzes the input image and closes together the pixels that are enclosed in each region to generate a soft segmented image. Adjacency and texture information are encountered in the soft segmentation stage. Soft segmented image is then fed to the hard segmentation process to generate the final segmentation result. As the proposed method is based on CLA it can adapt to its environment after some iterations. This adaptive behavior leads to a semi content-based segmentation process that performs well even in presence of... 

Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation. Image segmentation methods have proven to help quantify the disease and even help predict the outcome. The availability of longitudinal CT series may also result in an efficient and effective method to reliably assess the progression of COVID-19, monitor the healing process and the response to different therapeutic strategies. In this paper, we propose a new framework to identify infection at a voxel level (identification of healthy lung, consolidation, and ground-glass opacity) and visualize the... 

The development of computer-aided diagnosis (CAD) for breast magnetic resonance (MR) images has encountered some big challenges. One of these challenges is related to breast lesion segmentation. Accurate segmentation of breast lesions has a vital role in other consequent applications such as feature extraction. Since malignant breast lesions typically appear with irregular borders and shapes in MR images whereas benign masses appear with more regular shapes, and smooth and lobulated borders, it seems that the accurate segmentation of breast lesion borders in MR images are important. To achieve this purpose, we have used the Gradient Vector Flow (GVF) snake segmentation method. This study... 

Clustering-based image segmentation is a well-known multispectral image segmentation method. However, as it inherently does not account for the spatial relation among image pixels, it often results in inhomogeneous segmented regions. The recently proposed membership-connectedness (MC)-based segmentation method considers the local and global spatial relations besides the fuzzy clustering stage to improve segmentation accuracy. However, the inherent spatial and intraclass redundancies in multispectral images might decrease the accuracy and efficiency of the method. This letter addresses these two problems and proposes a segmentation method that is based on the MC method, watershed transform,... 

In the image segmentation field, traditional techniques do not completely meet the segmentation challenges mostly posed by inherently fuzzy images. Fuzzy connectedness and fuzzy clustering are considered as two well-known techniques for introducing fuzzy concepts to the problem of image segmentation. Fuzzy connectedness-based Segmentation methods consider spatial relation of image pixels by "hanging togetherness" a notion based on intensity homogeneity. But, they do not inherently utilize feature information of image pixels. On the other hand, as the segmentation domain of fuzzy clustering-based methods is the feature space they do not consider spatial relations among image pixels. Recently,... 

The main problem with airway segmentation methods which significantly influences their accuracy is leakage into the extra-luminal regions due to thinness of the airway wall during the process of segmentation. This phenomenon potentially makes large regions of lungparenchyma to be wrongly identified as airways. A solution to this problem in the previous methods was based on leak detection followed by reducing leakage during the segmentation process. This has been dealt with adjusting the segmentation parameters and performing the re-segmentation process on the pre-segmented area. This makes the algorithm very exhaustive and more dependent on the user interaction. The method presented here is...