A Region Growing Vessel Segmentation Algorithm Based on Spectrum Information

Jiang, Huiyan; He, Baochun; Fang, Di Fang Fang; Ma, Zhiyuan; Yang, Benqiang; Zhang, Libo

doi:10.1155/2013/743870

Cited by 21 publications

(11 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The complete modeling is carried out considering statistical approach. Adoption of region growing technique has also been reported in the work carried out by Jiang et al [34]. Thresholdingbased segmentation scheme was witnessed to offer better system performance during segmentation process as seen in the work of Liu et al [35].…”

Section: Related Workmentioning

confidence: 92%

Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions

Savitha¹

2019

IJECE

View full text Add to dashboard Cite

Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system.

show abstract

Section: Related Workmentioning

confidence: 92%

Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions

Savitha¹

2019

IJECE

View full text Add to dashboard Cite

show abstract

“…Specifically, this approach iteratively analyzes the neighboring initial-seed-point pixels and determines whether the pixel neighbors ought to be added to the region. It has been used for several segmentation tasks including retinal-vessel segmentation [18], vessel-segmentation algorithm based on spectrum information [19], and color image segmentation and for segmentation of synthetic aperture radar [20,21].…”

Section: Lymph-node Segmentationmentioning

confidence: 99%

Semi-automatic lymph node segmentation and classification using cervical cancer MR imaging

Bnouni

Mechi

Rekik

et al. 2018

2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

View full text Add to dashboard Cite

The segmentation and classification of Lymph Nodes (LNs) is a fundamental but challenging step in the analysis of medical images of cervical cancer. Both tasks can leverage morphological features such as size, shape, contour, and heterogeneous appearance. However, these features might vary with the progressive state of LNs. Hence, accurate detection of LNs boundary is an essential step sing to classify LN as suspect (malignant) and non-suspect (benign). However, manual delineation of LNs might produce classification errors due to the inter and intra-observer variability. Semi-automatic and automatic LNs segmentation methods are greatly desired as they would help improve patient diagnosis and treatment processes. Currently, Magnetic Resonance Imaging (MRI) is widely used to diagnose cervical cancer and LN involvement. Diffusion Weighted (DW)-MRI exhibits metastatic LN as bright regions. This paper presents a semi-automatic segmentation and classification method of LNs. Specifically, we propose a novel approach which leverages (1) the complementarity of structural and diffusion MR images through a fusion step and (2) morphological features of the segmented metastatic LNs for classification. The contribution of our proposed algorithm is threefold. First, we fuse the axial T2-Weighted (T2-w) anatomical image and the DW image. Second, we detect LNs using region-growing method in order to compute the final classification. Third, segmentation results are then used to classify LNs based on a gray level dependency matrix technique which extracts LN features. We evaluated our method using 10 MR images T2-w and DW with 47 metastatic LNs. We obtained an average accuracy of 70.21% for cervical cancer nodule classification.

show abstract

“…In [6], a region growing vessel segmentation algorithm based on spectrum information was introduced which applied Fourier transform on the Region of Interest (ROI). ROI included vascular structures to gain spectrum information according to extracted primary features.…”

Section: Related Workmentioning

confidence: 99%

New Technique for Automatic Segmentation of Blood Vessels in CT Scan Images of Liver Based on Optimized Fuzzy C-Means Method

Ahmadi

Karimi

Nia

2016

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Automatic segmentation of medical CT scan images is one of the most challenging fields in digital image processing. The goal of this paper is to discuss the automatic segmentation of CT scan images to detect and separate vessels in the liver. The segmentation of liver vessels is very important in the liver surgery planning and identifying the structure of vessels and their relationship to tumors. Fuzzy C-means (FCM) method has already been proposed for segmentation of liver vessels. Due to classical optimization process, this method suffers lack of sensitivity to the initial values of class centers and segmentation of local minima. In this article, a method based on FCM in conjunction with genetic algorithms (GA) is applied for segmentation of liver's blood vessels. This method was simulated and validated using 20 CT scan images of the liver. The results showed that the accuracy, sensitivity, specificity, and CPU time of new method in comparison with FCM algorithm reaching up to 91%, 83.62, 94.11%, and 27.17 were achieved, respectively. Moreover, selection of optimal and robust parameters in the initial step led to rapid convergence of the proposed method. The outcome of this research assists medical teams in estimating disease progress and selecting proper treatments.

show abstract

A Region Growing Vessel Segmentation Algorithm Based on Spectrum Information

Cited by 21 publications

References 10 publications

Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions

Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions

Semi-automatic lymph node segmentation and classification using cervical cancer MR imaging

New Technique for Automatic Segmentation of Blood Vessels in CT Scan Images of Liver Based on Optimized Fuzzy C-Means Method

Contact Info

Product

Resources

About