The aim of this work is the revelation of the possibility of the use of texture analysis methods to detection and segmentation tumor tissue in patient’s lungs and classification viable areas of tumor tissue. The main assumption includes the possibility that there are differences of textural properties between tumor and surrounding tissues and changes of these features during development and treatment of this disease. The work deals with the creation of vector of texture features which is composed of some methods of texture analysis and then processed by methods of cluster analysis in programming environment Matlab®
The presence of tumour heterogeneity makes the clinical oncological practice very challenging, since...
Background Texture analysis and machine learning methods are useful in distinguishing between benign...
The lung cancer is the principle cause of the worldwide deaths and its prognosis is poor with a 5-ye...
The aim of this work is the revelation of the possibility of the use of texture analysis methods to ...
Current development of algorithms in computer-aided diagnosis (CAD) scheme is growing rapidly to ass...
Lung cancer is the leading cause of cancer mortality worldwide. Treatment pathways include regular c...
The study is devoted to the analysis of dynamic changes in computer tomography (CT) images of lungs,...
Texture analysis of computed tomography (CT) imaging has been found useful to distinguish subtle dif...
In computer vision applications including object recognition, surface defect detection, pattern reco...
The Haralick texture features are a well-known mathematical method to detect the lung abnormalities ...
The radiology examination by computed tomography (CT) scan is an early detection of lung cancer to m...
Medical image enhancement & classification play an important role in medical research area. To a...
Medical image enhancement & classification play an important role in medical research area. To a...
The general objective of the thesis is automation of the analysis of the pathological lung from CT ...
The screen-capture image shows the brief process of texture analysis using the in-house software pro...
The presence of tumour heterogeneity makes the clinical oncological practice very challenging, since...
Background Texture analysis and machine learning methods are useful in distinguishing between benign...
The lung cancer is the principle cause of the worldwide deaths and its prognosis is poor with a 5-ye...
The aim of this work is the revelation of the possibility of the use of texture analysis methods to ...
Current development of algorithms in computer-aided diagnosis (CAD) scheme is growing rapidly to ass...
Lung cancer is the leading cause of cancer mortality worldwide. Treatment pathways include regular c...
The study is devoted to the analysis of dynamic changes in computer tomography (CT) images of lungs,...
Texture analysis of computed tomography (CT) imaging has been found useful to distinguish subtle dif...
In computer vision applications including object recognition, surface defect detection, pattern reco...
The Haralick texture features are a well-known mathematical method to detect the lung abnormalities ...
The radiology examination by computed tomography (CT) scan is an early detection of lung cancer to m...
Medical image enhancement & classification play an important role in medical research area. To a...
Medical image enhancement & classification play an important role in medical research area. To a...
The general objective of the thesis is automation of the analysis of the pathological lung from CT ...
The screen-capture image shows the brief process of texture analysis using the in-house software pro...
The presence of tumour heterogeneity makes the clinical oncological practice very challenging, since...
Background Texture analysis and machine learning methods are useful in distinguishing between benign...
The lung cancer is the principle cause of the worldwide deaths and its prognosis is poor with a 5-ye...