Results 101 to 110 of about 22,865 (233)

Glioma Grade Classification Using Machine Learning and MRI Radiomics: A Single‐Center Prospective Study Comparing Original and Wavelet‐Transformed Features From Anatomical, Diffusion‐Weighted, and Post‐Contrast Imaging

Health Science Reports, Volume 9, Issue 8, August 2026.
ABSTRACT Background and Aims Accurate glioma grade classification is critical for prognostic assessment and clinical decision‐making. This study aimed to evaluate the impact of wavelet‐based radiomics feature analysis on the performance of machine learning (ML) models for glioma grade classification using diffusion‐weighted imaging (DWI), structural ...
Amir Khorasani, Daryoush Shahbazi‐Gahrouei   +1 more
wiley   +1 more source

Assessment of Robustness of MRI Radiomic Features in Four Abdominal Organs: Impact of Deep Learning Reconstruction and Segmentation

Journal of Magnetic Resonance Imaging, Volume 64, Issue 2, Page 468-479, August 2026.
ABSTRACT Background The impact of deep learning (DL) reconstruction and segmentation on MRI radiomics stability has not been fully assessed. Purpose To investigate the effects of acquisition, reconstruction, and segmentation on the reproducibility and variability of radiomic features in abdominal MRI. Study Type Prospective.
Jingyu Zhong, Yue Xing, Yangfan Hu, Xianwei Liu, Shun Dai, Defang Ding, Junjie Lu, Jiarui Yang, Yue Li, Yang Song, Minda Lu, Dominik Nickel, Wenjie Lu, Huan Zhang, Weiwu Yao   +14 more
wiley   +1 more source

GLCM and PSNR Analysis of Woven Fabric Images Made from Natural Dyes Due to Sunlight Exposure

JOIV: International Journal on Informatics Visualization
Traditional woven fabrics generally use natural dyes that come from the local area. Natural dyes are often considered low quality if exposed to sunlight.
Patrisius Batarius, Albertus Joko Santoso, Alfry Aristo Jansen Sinlae   +2 more
doaj   +1 more source

3D Radiomic Texture Analysis of Quantitative Muscle MRI Enhances the Distinction Between Myotonic Dystrophy Type 1 and Charcot–Marie‐Tooth Neuropathy Type 1A: A Proof‐of‐Concept Study

European Journal of Neurology, Volume 33, Issue 8, August 2026.
In this proof‐of‐concept study, 3D radiomic texture analysis of quantitative muscle MRI (proton density fat fraction (PDFF, %) maps) distinguished the myogenic disease DM1 from the neurogenic disease CMT1A. Compared with DM1, CMT1A showed higher entropy, contrast, and lower homogeneity, reflecting a reticular vs.
Louise Iterbeke, Lotte Huysmans, Kobe Bamps, Ronald Peeters, Veerle Goosens, Frederik Maes, Patrick Dupont, Kristl G. Claeys   +7 more
wiley   +1 more source

Performance Comparison of GLCM Features and Preprocessing Effect on Batik Image Retrieval

JOIV: International Journal on Informatics Visualization
The use of the Grey-Level Co-occurrence Matrix (GLCM) for feature extraction in image retrieval with complex motifs, such as batik images, has been widely used. Some features often extracted include energy, entropy, correlation, and contrast.
Yufis Azhar, Denar Regata Akbi
doaj   +1 more source

An Efficient Steganalytic Algorithm based on Contourlet with GLCM

Research Journal of Applied Sciences, Engineering and Technology, 2014
Steganalysis is a technique to detect the hidden embedded information in the provided data. This study proposes a novel steganalytic algorithm which distinguishes between the normal and the stego image. III level contourlet is exploited in this study. Contourlet is known for its ability to capture the intrinsic geometrical structure of an image.
T.J. Benedict Jose, P. Eswaran
openaire   +1 more source

Application of Feature Extraction and Classification Methods for Histopathological Image using GLCM, LBP, LBGLCM, GLRLM and SFTA

, 2018
Classification of histopathologic images and identification of cancerous areas is quite challenging due to image background complexity and resolution. The difference between normal tissue and cancerous tissue is very small in some cases. So, the features
Ş. Öztürk, B. Akdemir
semanticscholar   +1 more source

Fabric Defect Classification Based on LBP and GLCM

Journal of Fiber Bioengineering and Informatics, 2015
Inevitably there will be various types of fabric defect exists in textile production line. In order to distinguish and classify the types of defects more efficiently and accurately, an algorithm which combines Local Binary Patterns (LBP) and Gray-level Co-occurrence Matrix (GLCM) is proposed in this paper for fabric defect classification.
Lei Zhang, Junfeng Jing null, Hongwei Zhang   +2 more
openaire   +2 more sources

Analisis Fitur Citra untuk Deteksi Kanker Prostat Menggunakan GLCM dan T-Test

Medika Teknika
Kanker prostat merupakan salah satu kelainan paling umum pada kelenjar prostat yang dapat menyebabkan gangguan buang air kecil hingga nyeri tulang akibat penyebaran ke tulang.
Mhd. Hanafi, Hendra Setiawan, Yessi Jusman   +2 more
doaj   +1 more source

Detection Of Glaucoma Using Glcm And Mst Segmentation

İlköğretim Online, 2023
Glaucoma is a globaleye disease that leads to blindness. This is the second leading cause of vision loss.If it left untreated the patient may lose vision, and even become blind.But blindness from glaucoma can often be prevented with early treatment. Existing Scanning methods like OCT, SLP, HRT has been used for detection of glaucoma but these methods ...
C.Lekha, R.Keerthivasan, C.Thirukumaran, A.Manikandan   +3 more
openaire   +1 more source