New algorithms to Enhanced Fused Images from Auto-Focus Images

Abbas, Heba Kh.; Al-Saleh, Anwar H.; Mohamad, Haidar J.; Al-Zuky, Ali A.

doi:10.21123/bsj.2021.18.1.0124

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…, where L -local information, which define by local geometric properties, G -global property of front depends on contour and location of front; I -independent properties that independent of front [11].…”

Section: Levelsets Algorithmmentioning

confidence: 99%

Automatic pancreas segmentation using ResNet-18 deep learning approach

Kakarwal¹,

Paithane²

2022

SRIT

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The accurate pancreas segmentation process is essential in the early detection of pancreatic cancer. The pancreas is situated in the abdominal cavity of the human body. The abdominal cavity contains the pancreas, liver, spleen, kidney, and adrenal glands. Sharp and smooth detection of the pancreas from this abdominal cavity is a challenging and tedious job in medical image investigation. Top-down approaches like Novel Modified K-means Fuzzy clustering algorithm (NMKFCM), Scale Invariant Feature Transform (SIFT), Kernel Density Estimator (KDE) algorithms were applied for pancreas segmentation in the early days. Recently, Bottom-up method has become popular for pancreas segmentation in medical image analysis and cancer diagnosis. LevelSet algorithm is used to detect the pancreas from the abdominal cavity. The deep learning, bottom-up approach performance is better than another. Deep Residual Network (ResNet-18) deep learning, bottom-up approach is used to detect accurate and sharp pancreas from CT scan medical images. 18 layers are used in the architecture of ResNet-18. The automatic pancreas and kidney segmentation is accurately extracted from CT scan images. The proposed method is applied to the medical CT scan images dataset of 82 patients. 699 images and 150 images with different angles are used for training and testing purposes, respectively. ResNet-18 attains a dice similarity index value up to 98.29±0.63, Jaccard Index value up to 96.63±01.25, Bfscore value up to 84.65±03.96. The validation accuracy of the proposed method is 97.01%, and the loss rate value achieves up to 0.0010. The class imbalance problem is solved by class weight and data augmentation.

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Section: Levelsets Algorithmmentioning

confidence: 99%

Automatic pancreas segmentation using ResNet-18 deep learning approach

Kakarwal¹,

Paithane²

2022

SRIT

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“…Image fusion is the process of merging information between two or more images to obtain a fused image with integral information, high quality, and other advantages; it can be performed using different methods, such as pixel-level fusion, feature-level fusion, and decision-level fusion, depending on the specific application and requirements [1]. It saves time and reduces costs by acquiring multiple images of the same scene or object.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Blurry Color Image Fusion Techniques using Color Transform

Mohammed,

Mohamad,

Abbas

et al. 2023

Al-Mustansiriyah Journal of Science

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This work focuses on fused color images resulting from motion blur (left and right) with a blur block size of 11 pixels. The color conversion process was performed from RGB color space (Red, Green, Blue) to HSV (Hue, Saturation, Value), L*a*b*, and Ycbcr (Luminance, Chrominance) color space. The traditional (addition, multiplication) and proposed fusion techniques (absolute real standard deviation) were used for this purpose. The data was examined by quality criteria with reference (Mutual Information, Correlation Coefficient, Structural Content, Normalized Cross Correlation) and without reference (Blind Reference less Image Spatial Quality Evaluator, Naturalness Image Quality Evaluator, and Perception-based Image Quality Evaluator). In results and depending on the criteria, the best fusion method is the proposed real standard deviation.

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Exploring Text Data Compression: A Comparative Study of Adaptive Huffman and LZW Approaches

Kadhim,

Mosleh,

Abed

2024

BIO Web Conf.

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Data compression is a critical procedure in computer science that aims to minimize the size of data files while maintaining their vital information. It is extensively utilized in Numerous applications, including communication, data storage, and multimedia transmission. In this work, we investigated the results of compressing four different text files with Lempel-Ziv-Welch compression techniques and Adaptive Huffman coding. The experiment used four text files: Arabic and English paragraphs and repeated Arabic and English characters. We measured Bit-rate, Compression Time, and Decompression Time to evaluate the algorithms' performance. With a compression time of around 22 μsec/char, the results demonstrated that the Adaptive Huffman algorithm was quicker at compressing Arabic and English text files. On the other hand, the decompression time for the LZW technique was 23 μsec/char, which was quicker. The Adaptive Huffman algorithm outperforms the LZW with a Bit rate of about 1.25 bits per character for Arabic text. The English-formatted encoded text's Bit rate in Adaptive Huffman was 4.495 bit/char, lower than LZW's Bit rates of 3.363 and 6.824 bit/char for the Arabic and English texts, respectively. When it came to texts containing Arabic and English characters, the LZW algorithm outperformed the Adaptive Huffman algorithm in terms of decompression time and Bit-rate. The decompression time for a text with Arabic letters was 6 μsec/char, and the Bit-rate was 0.717 bits/char. These values were lower compared to the text with English letters, which had a decompression time of 16 μsec/char and a Bit-rate of 1.694 bit/char. For compression time Adaptive Huffman outperform LZW and achieve 15 μsec/char, and 47 μsec/char for both Arabic and English letters files respectively.

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New algorithms to Enhanced Fused Images from Auto-Focus Images

Cited by 4 publications

References 17 publications

Automatic pancreas segmentation using ResNet-18 deep learning approach

Automatic pancreas segmentation using ResNet-18 deep learning approach

Quantitative Analysis of Blurry Color Image Fusion Techniques using Color Transform

Exploring Text Data Compression: A Comparative Study of Adaptive Huffman and LZW Approaches

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