Texture analysis within contrast enhanced abdominal CT images

Ciurte, Anca; Nedevschi, Sergiu

doi:10.1109/iccp.2009.5284781

Cited by 10 publications

(8 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For this purpose, the characteristics of higher order can be considered to incorporate spatial relation between pixels for the texture analysis of lung using CT images. The second order statistics, in the work namely the gray level co-occurrence matrix, is obviously suitable for texture characterization precisely because of their ability to define the joint probability distribution of pixel pairs [4].…”

Section: Second Order Statisticsmentioning

confidence: 99%

A statistical description of 3D lung texture from CT data

Chaisaowong

Paul

2015

SPIE Proceedings

View full text Add to dashboard Cite

A method was described to create a statistical description of 3D lung texture from CT data. The second order statistics, i.e. the gray level co-occurrence matrix (GLCM), has been applied to characterize texture of lung by defining the joint probability distribution of pixel pairs. The required GLCM was extended to three-dimensional image regions to deal with CT volume data. For a fine-scale lung segmentation, both the 3D GLCM of lung and thorax without lung are required. Once the co-occurrence densities are measured, the 3D models of the joint probability density function for each describing direction of involving voxel pairs and for each class (lung or thorax) are estimated using mixture of Gaussians through the expectation-maximization algorithm. This leads to a feature space that describes the 3D lung texture.

show abstract

Section: Second Order Statisticsmentioning

confidence: 99%

A statistical description of 3D lung texture from CT data

Chaisaowong

Paul

2015

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

“…Textural features include Gabor energy, homogeneity, energy, contrast, and correlation. Statistical features may include uniformity, entropy, and mean grey-level intensity between healthy/tumor tissues [12]. The advanced classification systems aim to classify the liver tumors into more specific categories, such as normal, cyst, hemangioma, and HCC [13].…”

Section: Introductionmentioning

confidence: 99%

Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans

Al-Saeed¹,

Gab-Allah²,

Soliman³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

One of the leading causes of mortality worldwide is liver cancer. The earlier the detection of hepatic tumors, the lower the mortality rate. This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors. Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range, intensity values overlap between the liver and neighboring organs, high noise from computed tomography scanner, and large variance in tumors shapes. The proposed method consists of three main stages; liver segmentation using Fast Generalized Fuzzy C-Means, tumor segmentation using dynamic thresholding, and the tumor's classification into malignant/benign using support vector machines classifier. The performance of the proposed system was evaluated using three liver benchmark datasets, which are MICCAI-Sliver07, LiTS17, and 3Dircadb. The proposed computer adided diagnosis system achieved an average accuracy of 96.75%, sensetivity of 96.38%, specificity of 95.20% and Dice similarity coefficient of 95.13%.

show abstract

“…The problems are due to in‐homogenous intensity values in liver parenchyma, similar gray‐level distributions with surrounding organs and variation in textural characteristics, which limits performance of statistical texture analysis. Therefore, NN‐based methods require a large number of training data sets. Experimental results of the hierarchical NN‐based approach showed that liver segmentation from CT angiography datasets takes 8–17 min in a standard PC (8 GB Ram, 2.15 GB memory, Pentium i7 processor).…”

Section: Introductionmentioning

confidence: 99%

Fully automated liver segmentation using Sobolev gradient‐based level set evolution

Göçeri

2016

Numer Methods Biomed Eng

View full text Add to dashboard Cite

Quantitative analysis and precise measurements on the liver have vital importance for pre-evaluation of surgical operations and require high accuracy in liver segmentation from all slices in a data set. However, automated liver segmentation from medical image data sets is more challenging than segmentation of any other organ due to various reasons such as vascular structures in the liver, high variability of liver shapes, similar intensity values, and unclear edges between liver and its adjacent organs. In this study, a variational level set-based segmentation approach is proposed to be efficient in terms of processing time and accuracy. The efficiency of this method is achieved by (1) automated initialization of a large initial contour, (2) using an adaptive signed pressure force function, and also (3) evolution of the level set with Sobolev gradient. Experimental results show that the proposed fully automated segmentation technique avoids local minima and stops evolution of the active contour at desired liver boundaries with high speed and accuracy. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Texture analysis within contrast enhanced abdominal CT images

Cited by 10 publications

References 4 publications

A statistical description of 3D lung texture from CT data

A statistical description of 3D lung texture from CT data

Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans

Fully automated liver segmentation using Sobolev gradient‐based level set evolution

Contact Info

Product

Resources

About