Liver segmentation using marker controlled watershed transform

Napte, Kiran Malhari; Mahajan, Anurag

doi:10.11591/ijece.v13i2.pp1541-1549

Cited by 3 publications

(2 citation statements)

References 31 publications

(40 reference statements)

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“…The segmentation performance of the proposed ESP-Net for sample images from the LiTS dataset is illustrated in Fig. The outcomes of the ESP-UNet are compared with the previous traditional state of arts for liver segmentation such as ACM-ResNet [16], H-DenseNet [17], and MSN-Net [15] based on DSC, VOE, JI, and RDV. Table II shows the results of the proposed liver segmentation using ESP-UNet.…”

Section: A Performance Evaluation Of Esp-unet-based Liver Segmentationmentioning

confidence: 99%

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Automatic Liver Cancer Detection Using Deep Convolution Neural Network

Napte,

Mahajan,

Urooj

2023

IEEE Access

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Automatic liver cancer detection (ALCD) is very crucial in automatic biomedical image analysis diagnosis as it is the largest organ in the body and plays a significant role in the metabolic process as well as the elimination of toxins. In the last decade, various machine and deep learning schemes have been investigated for automatic ALCD using computed tomography (CT) images. However, ALCD in CT images is challenging because of the noise, intricate structure of abdominal computed tomography (CT) images, and textural changes throughout the CT images making liver segmentation a vital challenge that may result in both under-segmentation (u-seg) and over-segmentation (o-seg) of the organ. This paper presents liver segmentation based on the proposed Edge Strengthening Parallel UNet (ESP-UNet) for liver segmentation to avoid the u-seg and o-seg of the liver in CT images. Further, it offered ALCD based on lightweight sequential Deep Convolution Neural Networks (DCNN). The consequences of ESP-UNet DCNN-based ALCD are evaluated based on accuracy, recall, precision, and F1-score. The suggested approach provides a noteworthy improvement in ALCD over the traditional state of arts.

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Section: A Performance Evaluation Of Esp-unet-based Liver Segmentationmentioning

confidence: 99%

“…As a direct consequence of this, CT scans are commonly used to investigate the abdominal organs. Liver segmentation in abdominal CT imaging is a crucially important difficulty [16,17], because of the inadequate contract between the vacillating liver region and the adjacent organs such as the kidney, stomach, and pancreas [18,19,20].…”

Section: Introductionmentioning

confidence: 99%

Automatic Liver Cancer Detection Using Deep Convolution Neural Network

Napte,

Mahajan,

Urooj

2023

IEEE Access

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Efficient artificial intelligence approaches for medical image processing in healthcare: comprehensive review, taxonomy, and analysis

Alnaggar,

Jagadale,

Saif

et al. 2024

Artif Intell Rev

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In healthcare, medical practitioners employ various imaging techniques such as CT, X-ray, PET, and MRI to diagnose patients, emphasizing the crucial need for early disease detection to enhance survival rates. Medical Image Analysis (MIA) has undergone a transformative shift with the integration of Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL), promising advanced diagnostics and improved healthcare outcomes. Despite these advancements, a comprehensive understanding of the efficiency metrics, computational complexities, interpretability, and scalability of AI based approaches in MIA is essential for practical feasibility in real-world healthcare environments. Existing studies exploring AI applications in MIA lack a consolidated review covering the major MIA stages and specifically focused on evaluating the efficiency of AI based approaches. The absence of a structured framework limits decision-making for researchers, practitioners, and policymakers in selecting and implementing optimal AI approaches in healthcare. Furthermore, the lack of standardized evaluation metrics complicates methodology comparison, hindering the development of efficient approaches. This article addresses these challenges through a comprehensive review, taxonomy, and analysis of existing AI-based MIA approaches in healthcare. The taxonomy covers major image processing stages, classifying AI approaches for each stage based on method and further analyzing them based on image origin, objective, method, dataset, and evaluation metrics to reveal their strengths and weaknesses. Additionally, comparative analysis conducted to evaluate the efficiency of AI based MIA approaches over five publically available datasets: ISIC 2018, CVC-Clinic, 2018 DSB, DRIVE, and EM in terms of accuracy, precision, Recall, F-measure, mIoU, and specificity. The popular public datasets and evaluation metrics are briefly described and analyzed. The resulting taxonomy provides a structured framework for understanding the AI landscape in healthcare, facilitating evidence-based decision-making and guiding future research efforts toward the development of efficient and scalable AI approaches to meet current healthcare needs.

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