An integrated method for hemorrhage segmentation from brain CT Imaging

Bhadauria, H.S.; Singh, Annapurna; Dewal, M. L.

doi:10.1016/j.compeleceng.2013.04.010

Cited by 49 publications

(39 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…False Negative or FN means a pixel appears in ground truth hemorrhage region but it is absent from hemorrhagic region detected by the method [16][17].…”

Section: Materials and Methodologymentioning

confidence: 98%

Comparative analysis of various segmentation methods for brain hemorrhage detection using CT images

Bhadauria¹,

Patel²,

Bist³

et al. 2014

2014 9th International Conference on Industrial and Information Systems (ICIIS)

View full text Add to dashboard Cite

Brain hemorrhage which happens as a result of bleeding due to rupturing of blood vessels within the brain matter is the main causes of human death. This discharge of blood may disturb the normal circulation of the brain. Computed Tomography (CT) has been the primary modality for the detection of brain hemorrhages because it is widely available, less expensive, quick to perform, and depicts rapidly. However intensity inhomogeneity, worst tissue contrast and weak boundaries in CT images may cause significant difficulties in hemorrhage detection. Thus in this paper the performance of various segmentation methods for hemorrhage detection from brain CT images have been evaluated.

show abstract

“…False Negative or FN means a pixel appears in ground truth hemorrhage region but it is absent from hemorrhagic region detected by the method [16][17].…”

Section: Materials and Methodologymentioning

confidence: 98%

Comparative analysis of various segmentation methods for brain hemorrhage detection using CT images

Bhadauria¹,

Patel²,

Bist³

et al. 2014

2014 9th International Conference on Industrial and Information Systems (ICIIS)

View full text Add to dashboard Cite

show abstract

“…The initial contour of the level set was obtained by extracting the CC using the FCM method [22]. The objective function of the FCM method for partitioning x k f g N k¼1 into c number of clusters is given by:…”

Section: Fcm-based Level Set Methodsmentioning

confidence: 99%

“…A hybrid method for the extraction of the CC from MR images that uses adaptive mean shift clustering and a geometric active contour has been reported [3]. Recently, FCM methods have been used to obtain the initial contour for the segmentation of tumors from brain computed tomography images [21,22].…”

Section: Introductionmentioning

confidence: 99%

Automated Segmentation and Analysis of Corpus Callosum in Autistic MR Brain Images Using Fuzzy-c-Means-Based Level Set Method

2015

View full text Add to dashboard Cite

In this work, the segmentation and analysis of the corpus callosum (CC) in autistic MR brain images is carried out using a fuzzy c-means (FCM)-based level set method. Initially, the images are skull-stripped using the geodesic active contour method. The CC is extracted from the skull-stripped images using the FCM-based level set method. FCM clustering forms the initial contour. The evolution of the curve is then regularized using a distance function in the level sets. The segmented CC is divided into five segments, whose areas are measured. The subjective results show that the proposed method is able to extract the CC from skull-stripped images. It is demonstrated that the level set with the FCM as the initial contour gives better results than those obtained with a manual initial contour. It is found that the autistic subjects have a reduced CC area compared to that of control subjects. The total CC area of autistic subjects gives a correlation of R = 0.39 with the verbal intelligence quotient (IQ) values. Further analysis shows that the anterior third region of the CC gives significant discrimination of the control and autistic subjects compared to the other segments. Its correlation (R) with verbal IQ is found to be 0.27 in autistic subjects. The feature area extracted from the CC and its segments are significant, hence the results may be clinically helpful in the mass screening of autistic subjects.

show abstract

“…Several hybrid methods have been proposed to segment regions in brain CT images. Bhadauria et al proposed combination of Fuzzy C-Means clustering and region-based active contour to segment the haemorrhage region [3]. They used clustering to find an initial mask and then propagate it to the haemorrhage boundaries using region-based active contour.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated Classification of CT Scans in Traumatic Brain Injury Patients

Qureshi¹

2015

IJCA

View full text Add to dashboard Cite

A 'silent epidemic' affecting millions worldwide every year is the Traumatic Brain Injury. Management of these patients essentially involves neuroimaging and noncontrast Computed Tomography (CT) scans are the first choice amongst doctors. However, interobserver variability, considered 'Achilles heel' amongst radiologists, can lead to missed diagnoses and grave consequences. This paper presents a hybrid approach for semi-automated classification of CT features according to Marshall CT Scheme. The proposed method uses template matching, artificial neural networks and active contours for segmentation of significant anatomical landmarks and estimation of haematoma volume on brain CT scans. The proposed method is efficient and robust in segmenting cross-sectional, noncontrast CT scans and has been evaluated on images from subjects with different ages and both genders. The hybrid method has an average ICC ≥ 0.97 and Jaccard Index ≥ 0.86 compared with the manual demarcations by radiology experts and performs better than the state of the art. Hence, the approach can be used to provide second opinions very close to the experts' intuition.

show abstract

An integrated method for hemorrhage segmentation from brain CT Imaging

Cited by 49 publications

References 17 publications

Comparative analysis of various segmentation methods for brain hemorrhage detection using CT images

Comparative analysis of various segmentation methods for brain hemorrhage detection using CT images

Automated Segmentation and Analysis of Corpus Callosum in Autistic MR Brain Images Using Fuzzy-c-Means-Based Level Set Method

Semi-automated Classification of CT Scans in Traumatic Brain Injury Patients

Contact Info

Product

Resources

About