Noise-robust method for image segmentation

Segmentation of noisy images is one of the most challenging problems in image analysis and any improvement of segmentation methods can highly influence the performance of many image processing applications. In automated image segmentation, the fuzzy c-means (FCM) clustering has been widely used because of its ability to model uncertainty within the data, applicability to multi-modal data and fairly robust behaviour. However, the standard FCM algorithm does not consider any information about the spatial linage context and is highly sensitive to noise and other imaging artefacts. Considering above mentioned problems, we developed a new FCM-based approach for the noise-robust fuzzy clustering and we present it in this paper. In this new iterative algorithm we incorporated both spatial and feature space information into the similarity measure and the membership function. We considered that spatial information depends on the relative location and features of the neighbouring pixels. The performance of the proposed algorithm is tested on synthetic image with different noise levels and real images. Experimental quantitative and qualitative segmentation results show that our method efficiently preserves the homogeneity of the regions and is more robust to noise than other FCM-based methods

Dental X-ray Image Segmentation using Gaussian Kernel-Based in Conditional Spatial Fuzzy C-means

Dental X-ray image segmentation is a difficult task because of intensity inhomogeneities among various regions, low image quality due to noise and low contrast errors of data scanning. In this paper, we proposed a new conditional spatial fuzzy C-means algorithm with Gaussian kernel function to facilitate dental X-ray image segmentation. The Gaussian kernel function is used as an objective function of conditional spatial fuzzy C-means algorithm to substitute the Euclidian distance. Performance evaluation of the proposed algorithm was carried on dental X-ray from different teeth of some panoramic radiographs. The average of false negative fraction (FNF) and false positive fraction (TPF) values using proposed algorithm better than conditional spatial fuzzy C-means algorithm but vise versa for true positive volume fraction (FPF) value. The segmentation result of the proposed algorithm effectively recognizes tooth region as main part of the dental X-ray image

Study of Segmentation Techniques for Medical Images

Segmentation techniques are widely used in for the application of Medical Images. These techniques play a crucial role by automating or facilitating the delineation of anatomical structures and other regions of interest . Image Segmentation is particularly dicult due to restrictions imposed by biological variation and image acquisition . Goal of medical image segmentation is to perform operations on medical images to identify patterns in the use of interaction and develop qualitative criteria to evaluate interactive segmentation methods and extract information from it .Techniques available for segmentation are specic to application and the segment of biological detail to be studied . Number of techniques require wide use of Algorithms specically for certain segmentation procedure of medical images . From the Medical Image processing point of view the classication of segmentation techniques is on gray level and texture based techniques. Approaches have been proposed to segment CT and MR Images. In the following chapters we discuss the reviewed methods with respect to all information provided by the user and the parameters for the computational part. Medical image segmentation techniques require some form of expert supervision to provide accurate and consistent identication of anatomic structures. A novel segmentation technique was developed that combines a knowledge-based segmentation system with a contour model. This method exploits the guidance of a higher level process to robustly perform the segmentation of kind of anatomic structures. Knowledge about the anatomic structures to be segmented is dened statistically in terms of probability density functions of parameters of location, size, and image intensity. The active contour based technique outperform the standard segmentation methods due to its capacity to fully enforce the available a priori knowledge concerning the anatomic structure of interest. The active contour algorithm is suitable for integration with high-level image understanding frameworks, and providing a robust low-level segmentation tool. Studies are required to determine whether the proposed algorithm is indeed capable of providing consistently superior segmentation

Advanced Brain Tumour Segmentation from MRI Images

Magnetic resonance imaging (MRI) is widely used medical technology for diagnosis of various tissue abnormalities, detection of tumors. The active development in the computerized medical image segmentation has played a vital role in scientific research. This helps the doctors to take necessary treatment in an easy manner with fast decision making. Brain tumor segmentation is a hot point in the research field of Information technology with biomedical engineering. The brain tumor segmentation is motivated by assessing tumor growth, treatment responses, computer-based surgery, treatment of radiation therapy, and developing tumor growth models. Therefore, computer-aided diagnostic system is meaningful in medical treatments to reducing the workload of doctors and giving the accurate results. This chapter explains the causes, awareness of brain tumor segmentation and its classification, MRI scanning process and its operation, brain tumor classifications, and different segmentation methodologies

Automatic MRI 2D Brain Segmentation using Graph SearchingTechnique

Accurate and efficient segmentation of the whole brain in magnetic resonance (MR) images is a key task in many neuroscience and medical studies either because the whole brain is the final anatomical structure of interest or because the automatic extraction facilitates further analysis. The problem of segmenting brain MRI images has been extensively addressed by many researchers. Despite the relevant achievements obtained, automated segmentation of brain MRI imagery is still a challenging problem whose solution has to cope with critical aspects such as anatomical variability and pathological deformation. In the present paper, we describe and experimentally evaluate a method for segmenting brain from MRI images basing on two-dimensional graph searching principles for border detection. The segmentation of the whole brain over the entire volume is accomplished slice by slice, automatically detecting frames including eyes. The method is fully automatic and easily reproducible by computing the internal main parameters directly from the image data. The segmentation procedure is conceived as a tool of general applicability, although design requirements are especially commensurate with the accuracy required in clinical tasks such as surgical planning and post-surgical assessment. Several experiments were performed to assess the performance of the algorithm on a varied set of MRI images obtaining good results in terms of accuracy and stabilit

Semi-automatic detection of hepatic tumor in computed tomography images

In this work, the main purpose is develop a computational segmentation strategy for liver tumor semiautomatic detection. This strategy considers three-dimensional computed tomography images and it consists of techniques application that, on the one hand, diminish the noise and detect the edges of the objects present in those images and, on the other hand, generate the liver tumor morphology. For this, the sequence of techniques composed of gaussian smoothing, gradient magnitude, median filter, region growing and binary morphological dilation are used. The value obtained, for the metric called Dice score, show a good correlation between manual segmentation, performed by a hepatologist, and the tumor segmentation obtained using the proposed technique. This type of segmentation is the extreme utility for the characterization of hepatic tumors and the planning of the clinical behavior to be followed in the treatment of this human liver disease