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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Robotics and Control (JRC)
Meziane, Farid
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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LICA-CS: Efficient Lossless Image Compression Algorithm via Column Subtraction Model Al Qerom, Mahmoud; Otair, Mohammad; Meziane, Farid; AbdulRahman, Sawsan; Alzubi, Maen
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.21834

Abstract

Driven by the unprecedented amount of data generated in the last few decades, data storage and communication are becoming more challenging. Although many approaches in data compression have been developed to alleviate these challenges, more efforts are still needed, especially for lossless image compression, which is a promising technique when critical information loss is not allowed. In this paper, we propose a new algorithm called the Lossless Image Compression Algorithm using a Column Subtraction model (LICA-CS). LICA-CS is efficient, low in complexity, decreases the image bit-depth, and enhances state-of-the-art performance. LICA-CS first implements a color transformation method as a pre-processing phase, which strategically minimizes inter-channel correlations to optimize compression outcomes. After that, a novel subtraction method was developed to compress the image data column-wise. We tackle the similarity and proximity of pixel values within adjacent columns, which offers a distinct advantage in reducing image size observing a significant size reduction of 71%. This is achieved through the subtraction of neighboring columns. The conducted experiments on colored images show that LICA-CS outperforms existing algorithms in terms of compression rate. Moreover, our method exhibited remarkable enhancements in execution time, with compression and decompression processes averaging 1.93 seconds. LICA-CS advances the state-of-the-art in lossless image compression, promising enhanced efficiency and effectiveness in image compression technologies.
LICA-CS: Efficient Lossless Image Compression Algorithm via Column Subtraction Model Al Qerom, Mahmoud; Otair, Mohammad; Meziane, Farid; AbdulRahman, Sawsan; Alzubi, Maen
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.21834

Abstract

Driven by the unprecedented amount of data generated in the last few decades, data storage and communication are becoming more challenging. Although many approaches in data compression have been developed to alleviate these challenges, more efforts are still needed, especially for lossless image compression, which is a promising technique when critical information loss is not allowed. In this paper, we propose a new algorithm called the Lossless Image Compression Algorithm using a Column Subtraction model (LICA-CS). LICA-CS is efficient, low in complexity, decreases the image bit-depth, and enhances state-of-the-art performance. LICA-CS first implements a color transformation method as a pre-processing phase, which strategically minimizes inter-channel correlations to optimize compression outcomes. After that, a novel subtraction method was developed to compress the image data column-wise. We tackle the similarity and proximity of pixel values within adjacent columns, which offers a distinct advantage in reducing image size observing a significant size reduction of 71%. This is achieved through the subtraction of neighboring columns. The conducted experiments on colored images show that LICA-CS outperforms existing algorithms in terms of compression rate. Moreover, our method exhibited remarkable enhancements in execution time, with compression and decompression processes averaging 1.93 seconds. LICA-CS advances the state-of-the-art in lossless image compression, promising enhanced efficiency and effectiveness in image compression technologies.
Improving the Segmentation of Colorectal Cancer from Histopathological Images Using a Hybrid Deep Learning Pipeline: A Case Study Idiri, Fahima; MEZIANE, Farid; BOUCHAL, Hakim
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1158

Abstract

Early and precise diagnosis of colorectal cancer plays a crucial role in enhancing patients' outcomes. Although histopathological assessment remains the reference standard for diagnosis, it is often lengthy and subject to variability between pathologists. This study aims to develop and evaluate a hybrid deep learning-based approach for the automated segmentation of Hematoxylin and Eosin-stained colorectal histopathology images. The work investigates how preprocessing strategies and architectural design choices influence the model’s ability to identify meaningful tissue patterns while preserving computational efficiency. Furthermore, it demonstrates the integration of a deep learning-based segmentation module into colorectal cancer diagnostic workflows. Several deep learning–based segmentation models with varying architectural configurations were trained and evaluated using a publicly available endoscopic biopsy histopathological hematoxylin and eosin image dataset. Preprocessing procedures were applied to generate computationally efficient image representations, thereby improving training stability and overall segmentation performance. The best-performing configuration achieved a segmentation accuracy of 0.97, reflecting consistent and reliable performance across samples. It accurately delineated cancerous tissue boundaries and effectively distinguished benign from malignant regions, demonstrating sensitivity to fine morphological details relevant to diagnosis. Strong agreement between predicted and expert-annotated regions confirmed the model’s reliability and alignment with expert assessments. Minimal overfitting was observed, indicating stable training behavior and robust generalization across different colorectal tissue types. In comparative evaluations, the model maintained high accuracy across all cancer categories and outperformed existing state-of-the-art approaches. Overall, these findings demonstrate the model’s robustness, efficiency, and adaptability, confirming that careful architectural and preprocessing optimization can substantially enhance segmentation quality and diagnostic reliability. The proposed approach can support pathologists by providing accurate tissue segmentation, streamlining diagnostic procedures, and improving clinical decision-making. This study underscores the value of optimized deep learning models as intelligent decision-support tools for efficient and consistent colorectal cancer diagnosis
Co-Authors AbdulRahman, Sawsan Al Qerom, Mahmoud Alzubi, Maen BOUCHAL, Hakim Idiri, Fahima Otair, Mohammad