Three Channels for Gray Level Co-occurrence Matrix (GLCM) to detect Acute Lymphoblastic Leukemia (ALL) Images

Muntasa, Arif; Yusuf, Muhammad

doi:10.1145/3425577.3425583

Cited by 3 publications

(6 citation statements)

References 16 publications

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“…Texture classification methods, such as GLCM and Local Binary Patterns (LBP), are applied in various fields including medical imaging (Korchiyne et al 2014;Muntasa and Yusuf 2020), remote sensing such as landscape classification (Gao 2011;Hall-Beyer 2017), industrial inspection, writer identification, video analysis (Lloyd et al 2017) and agriculture (Yogeshwari and Thailambal 2021) as well as steel production (Bharati et al 2004;Hsu et al 2018). LBP has been extensively reviewed by Liu et al (2017), A detailed review of textural features has been presented earlier by Cavalin and Oliveira (2017).…”

Section: Related Workmentioning

confidence: 99%

Matching method for mutated veneer sheet images using gray-level co-occurrence matrix features

Savolainen

2023

Eur. J. Wood Prod.

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This paper studies the tracking of wooden veneer sheets by matching their respective wet and dry colour images. The tracking of veneer sheets has proved to be a challenging task due to random mutations during processing in terms of color changes, the emergence of defects, and, occasionally, lost pieces of the veneer surface. The proposed matching procedure involves image segmentation with five different sizes, followed by segment-wise extraction of Gray Level Co-occurrence Matrix (GLCM) textural feature arrays, and their similarity comparisons respectively. A voting mechanism is introduced that allocates the correct match based on the majority. An optional shifting procedure is applied to match candidates with missing areas. The method is demonstrated and benchmarked using a real-world dataset sourced from the industry, comprising 2579 high-quality images of spruce veneer pairs obtained from peeling and drying. In comparison to earlier studies that employed randomized 50 pair sampling on the same dataset, our approach yields a matching accuracy of 99.41%, outperforming the previously reported 84.93%. These findings have relevance for researchers in wood image analytics and carry practical implications for large-scale, automated veneer production facilities seeking innovative ways to optimize their raw material usage.

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Section: Related Workmentioning

confidence: 99%

Matching method for mutated veneer sheet images using gray-level co-occurrence matrix features

Savolainen

2023

Eur. J. Wood Prod.

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“…Also, the experiment using the GLCM texture-shape feature obtained the combined classification accuracy at 89.8 % for the nucleus-cytoplasm region [2]. In addition, three channels of GLCM and testing sets using Manhattan and Euclidean Distance have been developed and produced 91.54% of maximum accuracy [3]. Furthermore, [4] proposed Otsu's thresholding, watershed-based segmentation, canny edge detection, and k-mean clustering for segmentation.…”

Section: Introductionmentioning

confidence: 99%

Multi Distance and Angle Models of the Gray Level Co-occurrence Matrix (GLCM) to Extract the Acute Lymphoblastic Leukemia (ALL) Images

2021

IJIES

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“…Arif Muntasa et al, [6] has presented a method to classify Acute Lymphoblastic Leukemia (ALL) using the Gray Level Co-occurrence Matrix (GLCM) and sixteen distance models, resulting in 192 features for each object. This method achieved an impressive accuracy rate of 96.97% with minimal false positives and negatives, outperforming other existing approaches.…”

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confidence: 99%

“…The recent advancements in Leukemia diagnosis present a range of methodologies, each contributing valuable insights to the domain. Arif Muntasa et al's work [6] utilizes the Gray Level Co-occurrence Matrix (GLCM) for feature extraction, whereas other studies, like that of Ghada Emam Atteia et al [10], delve into deep learning frameworks.…”

mentioning

confidence: 99%

“…One notable trend from the related research is the focus on robust feature extraction and optimization. Arif Muntasa et al's approach [6], while achieving high accuracy rates, extracts 192 features for each object. Such a comprehensive feature space, while detailed, might introduce redundancy, potentially leading to computational inefficiencies and overfitting.…”

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confidence: 99%

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Optimal Feature Selection from High-dimensional Fusion of Blood Smear Images for Leukemia Diagnosis

Pullaiah,

P.M.

2024

SCPE

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The goal of this study is to improve blood smear image-based blood cancer prediction through medical diagnostic advancements. Blood cancers, particularly leukemia, are challenging to diagnose because of the complexity of biological data and the dimensionality of medical images. There are interpretability and computational problems with each currently in use. We suggest the Random Forest-Recurrent Feature Elimination (RF-RFE) model to increase the precision and dependability of blood cancer diagnosis. This model integrates machine learning and image processing, optimizes feature selection and refinement from high-dimensional data, and applies the XGBoost algorithm to guarantee diagnosis accuracy. Recent model analysis reveals that RF-RFE performs better than them on a wide range of metrics. The RF-RFE offered a sensible, well-rounded strategy. More research on medical diagnostics is made possible by its adaptability in multi-class classification and effectiveness in handling high-dimensional feature values. The optimized feature set and computational efficiency of the model, which may enhance leukemia detection and diagnostics, are highlighted in this study.

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Three Channels for Gray Level Co-occurrence Matrix (GLCM) to detect Acute Lymphoblastic Leukemia (ALL) Images

Cited by 3 publications

References 16 publications

Matching method for mutated veneer sheet images using gray-level co-occurrence matrix features

Matching method for mutated veneer sheet images using gray-level co-occurrence matrix features

Multi Distance and Angle Models of the Gray Level Co-occurrence Matrix (GLCM) to Extract the Acute Lymphoblastic Leukemia (ALL) Images

Optimal Feature Selection from High-dimensional Fusion of Blood Smear Images for Leukemia Diagnosis

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