Segmentation of brain MR images using fuzzy sets and modified co-occurrence matrix

Maji, Pradipta; Kundu, Malay K.; Chanda, Bhabotosh

doi:10.1049/cp:20060551

Cited by 7 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The co-occurrence matrices have been widely used in texture analysis for identification of tissue or to detect the abnormality within an organ tissue and in segmentation problems [8], [9], [10].…”

Section: Image Featuresmentioning

confidence: 99%

Hemorrhage detection in MRI brain images using images features

Moraru¹,

Moldovanu²,

Bibicu³

et al. 2013

AIP Conference Proceedings

View full text Add to dashboard Cite

Abstract. The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid -sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

show abstract

Section: Image Featuresmentioning

confidence: 99%

Hemorrhage detection in MRI brain images using images features

Moraru¹,

Moldovanu²,

Bibicu³

et al. 2013

AIP Conference Proceedings

View full text Add to dashboard Cite

show abstract

“…A robust segmentation technique based on fuzzy set theory for brain MR images is proposed in Reference (115).…”

Section: Segmentation Of Brain Magnetic Resonance Imagementioning

confidence: 99%

Fuzzy Image Processing and Recognition

Pal¹

2007

Wiley Encyclopedia of Electrical and Electronics Engineering

View full text Add to dashboard Cite

This article describes various fuzzy set theoretic tools and explores their effectiveness in representing/describing various uncertainties that might occur in an image recognition system and the ways these uncertainties can be managed in making a decision. Some examples of uncertainties that often develop in the process of recognizing a pattern are given in the next section. The Image Ambiguity and Uncertainty Measures Section provides a definition of image and describes various fuzzy set theoretic tools for measuring information on grayness ambiguity and spatial ambiguity in an image. Concepts of bound functions and spect sets charactering the flexible in membership functions are discussed in Their applications to formulate some low level vision operations (e.g., enhancement, segmentation, skeleton extraction, and edge detection), whose outputs are crucial and responsible for the overall performance of a vision system, are then presented. Some real‐life applications (e.g., motion frame analysis, character recognition, remote sensing image analysis, content‐based image retrieval, and brain MR image segmentation) of these methodologies and tools are then described. Finally, conclusions and discussion are provided. Introduction Uncertainties in a Recognition System and Relevance of Fuzzy Set Theory Image Ambiguity and Uncertainty Measures Grayness Ambiguity Measures Flexibility in Membership Functions Some Examples of Fuzzy Image Processing Operations Some Applications Conclusions and Discussion Acknowledgment

show abstract

“…The investigations of medical images for computer-aided diagnosis and treatment often require segmentation as an initial stage. Medical image segmentation is a difficult and challenging task mainly due to the vague nature of the images [ 2 – 4 ]. Magnetic resonance imaging (MRI) is one of the most significant diagnostic imaging techniques, often used for the early detection of anomalous changes in tissues and organs [ 5 , 6 ], and also it allows a radiologist to produce an image covering the internal features of living tissue because it is a noninvasive imaging technique [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

MRI Brain Images Healthy and Pathological Tissues Classification with the Aid of Improved Particle Swarm Optimization and Neural Network

Sheejakumari

Gomathi

2015

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

The advantages of magnetic resonance imaging (MRI) over other diagnostic imaging modalities are its higher spatial resolution and its better discrimination of soft tissue. In the previous tissues classification method, the healthy and pathological tissues are classified from the MRI brain images using HGANN. But the method lacks sensitivity and accuracy measures. The classification method is inadequate in its performance in terms of these two parameters. So, to avoid these drawbacks, a new classification method is proposed in this paper. Here, new tissues classification method is proposed with improved particle swarm optimization (IPSO) technique to classify the healthy and pathological tissues from the given MRI images. Our proposed classification method includes the same four stages, namely, tissue segmentation, feature extraction, heuristic feature selection, and tissue classification. The method is implemented and the results are analyzed in terms of various statistical performance measures. The results show the effectiveness of the proposed classification method in classifying the tissues and the achieved improvement in sensitivity and accuracy measures. Furthermore, the performance of the proposed technique is evaluated by comparing it with the other segmentation methods.

show abstract

Segmentation of brain MR images using fuzzy sets and modified co-occurrence matrix

Cited by 7 publications

References 17 publications

Hemorrhage detection in MRI brain images using images features

Hemorrhage detection in MRI brain images using images features

Fuzzy Image Processing and Recognition

MRI Brain Images Healthy and Pathological Tissues Classification with the Aid of Improved Particle Swarm Optimization and Neural Network

Contact Info

Product

Resources

About