Assessing the Classification of Liver Focal Lesions by Using Multi-phase Computer Tomography Scans

2013 ◽  
pp. 80-91 ◽  
Author(s):  
Auréline Quatrehomme ◽  
Ingrid Millet ◽  
Denis Hoa ◽  
Gérard Subsol ◽  
William Puech
Keyword(s):  
Computer Tomography ◽  
Focal Lesions ◽  
Multi Phase

Tensor-based sparse representations of multi-phase medical images for classification of focal liver lesions

2020 ◽  
Vol 130 ◽  
pp. 207-215 ◽  
Author(s):  
Jian Wang ◽  
Jing Li ◽  
Xian-Hua Han ◽  
Lanfen Lin ◽  
Hongjie Hu ◽  
...  
Keyword(s):  
Medical Images ◽  
Sparse Representations ◽  
Focal Liver Lesions ◽  
Liver Lesions ◽  
Multi Phase

Vergleichende Diagnostik fokaler Leberveränderungen mittels Computer-Tomographie und szintigraphischer Methoden

1991 ◽  
Vol 30 (02) ◽  
pp. 43-54 ◽  
Author(s):  
B. Kurtz ◽  
W. Müller-Schauenburg ◽  
U. Feine ◽  
P. Reuland
Keyword(s):  
Computer Tomography ◽  
Focal Nodular Hyperplasia ◽  
Blood Pool ◽  
Strong Increase ◽  
Typical Pattern ◽  
Focal Lesions ◽  
Specific Diagnosis ◽  
Subsequent Decrease ◽  
Ultrasound Findings ◽  
Nodular Hyperplasia

The value of scintigraphic methods for the diagnosis of focal lesions of the liver (IDA, colloids, blood pool) in comparison to dynamic sequential computer tomography (CT) has been examined in this study. We found CT to be diagnostic in typical cases. For example, focal nodular hyperplasia is characterized by a rapid, strong increase and subsequent decrease after application of contrast medium (71 %), whereas hemangiomas show a delayed density increase mostly at the rim (20%). In the event of deviations from the typical pattern, scintigraphic methods have to be applied which then often yield a specific diagnosis, especially in hemangiomas, focal nodular hyperplasia, and adenomas (71%). Ultrasound findings indicating the possible presence of hemangiomas or focal nodular hyperplasia should lead to scintigraphic studies prior to CT, not only for reasons of economy.

Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans

2020 ◽  
Vol 58 (10) ◽  
pp. 2497-2515
Author(s):  
Donlapark Ponnoprat ◽  
Papangkorn Inkeaw ◽  
Jeerayut Chaijaruwanich ◽  
Patrinee Traisathit ◽  
Patumrat Sripan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Intrahepatic Cholangiocarcinoma ◽  
Ct Scans ◽  
Multi Phase

scholarly journals Deep Learning Provisions in the Matlab: Focus on CNN Facility

2019 ◽  
Vol 8 (9S3) ◽  
pp. 990-994
Keyword(s):  
Computer Tomography ◽  
Classification Accuracy ◽  
Lung Diseases ◽  
Back Propagation ◽  
Normal Lung ◽  
Complex Wavelet Transform ◽  
Segmented Image ◽  
Entire Lung ◽  
Propagation Network

Currently, Lung diseases are the major problem that affect the lungs which is an important the organs that allow us to survive through breathing. The diseases such as pleural effusion, Asthma, chronic bronchitis, and normal lung are detected and classified in this work. This paper presents a Computer Tomography (CT) Images of lungs for detection of diseases which is developed using ANN-BPN. The purpose of the work is to detect and classify the lung diseases by effective feature extraction through Dual-Tree Complex Wavelet Transform and GLCM Features. The entire lung is segmented from the Computer Tomography Images and the parameters are calculated from the segmented image. The parameters are calculated using GLCM. We Propose and evaluate the ANN-Back Propagation Network designed for classification of ILD patterns. The parameters gives the maximum classification Accuracy. After result we propose the Fuzzy clustering to segment the lesion part from abnormal lung.

scholarly journals Microstructural Classification of Bainitic Subclasses in Low-Carbon Multi-Phase Steels Using Machine Learning Techniques

Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1836
Author(s):  
Martin Müller ◽  
Dominik Britz ◽  
Thorsten Staudt ◽  
Frank Mücklich
Keyword(s):  
Machine Learning ◽  
Construction Material ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Second Phase ◽  
Sem Images ◽  
Material Development ◽  
Learning Techniques ◽  
Multi Phase

With its excellent property combinations and ability to specifically adjust tailor-made microstructures, steel is still the world’s most important engineering and construction material. To fulfill ever-increasing demands and tighter tolerances in today’s steel industry, steel research remains indispensable. The continuous material development leads to more and more complex microstructures, which is especially true for steel designs that include bainitic structures. This poses new challenges for the classification and quantification of these microstructures. Machine learning (ML) based microstructure classification offers exciting potentials in this context. This paper is concerned with the automated, objective, and reproducible classification of the carbon-rich second phase objects in multi-phase steels by using machine learning techniques. For successful applications of ML-based classifications, a holistic approach combining computer science expertise and material science domain knowledge is necessary. Seven microstructure classes are considered: pearlite, martensite, and the bainitic subclasses degenerate pearlite, debris of cementite, incomplete transformation product, and upper and lower bainite, which can all be present simultaneously in one micrograph. Based on SEM images, textural features (Haralick parameters and local binary pattern) and morphological parameters are calculated and classified with a support vector machine. Of all second phase objects, 82.9% are classified correctly. Regarding the total area of these objects, 89.2% are classified correctly. The reported classification can be the basis for an improved, sophisticated microstructure quantification, enabling process–microstructure–property correlations to be established and thereby forming the backbone of further, microstructure-centered material development.

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