Comparative Review on Traditional and Deep Learning Methods for Medical Image Segmentation

Khaniabadi, Shadi Mahmoodi; Ibrahim, Haidi; Huqqani, Ilyas Ahmad; Khaniabadi, Farzad Mahmoodi; Sakim, Harsa Amylia Mat; Teoh, Soo Siang

doi:10.1109/icsgrc57744.2023.10215402

Cited by 3 publications

(2 citation statements)

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“…Traditional segmentation methods have historically served as the building blocks of medical image analysis [32,34]. These methods include thresholding, region growing, and edge detection [35][36][37].…”

Section: Traditional Segmentation Methodsmentioning

confidence: 99%

“…These methods include thresholding, region growing, and edge detection [35][36][37]. Researchers conducted a comparative study of thresholding techniques for medical image segmentation [34,38] and highlighted their simplicity and limitations in handling complex intensity variations [39]. One facet of the research community has explored the challenges and prospects of region-growing-based segmentation [40], particularly in complex structures like brain tumor images [41,42].…”

Section: Traditional Segmentation Methodsmentioning

confidence: 99%

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Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Saifullah,

Dreżewski

2024

Applied Sciences

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Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study of the efficacy of particle swarm optimization (PSO) combined with histogram equalization (HE) preprocessing for medical image segmentation, focusing on lung CT scan and chest X-ray datasets. Best-cost values reveal the PSO algorithm’s performance, with HE preprocessing demonstrating significant stabilization and enhanced convergence, particularly for complex lung CT scan images. Evaluation metrics, including accuracy, precision, recall, F1-score/Dice, specificity, and Jaccard, show substantial improvements with HE preprocessing, emphasizing its impact on segmentation accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, and K-means, confirm the competitiveness of the PSO-HE approach, especially for chest X-ray images. The study also underscores the positive influence of preprocessing on image clarity and precision. These findings highlight the promise of the PSO-HE approach for advancing the accuracy and reliability of medical image segmentation and pave the way for further research and method integration to enhance this critical healthcare application.

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Section: Traditional Segmentation Methodsmentioning

confidence: 99%

Section: Traditional Segmentation Methodsmentioning

confidence: 99%

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Saifullah,

Dreżewski

2024

Applied Sciences

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CellSegUNet: an improved deep segmentation model for the cell segmentation based on UNet++ and residual UNet models

Metlek

2024

Neural Comput & Applic

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Cell nucleus segmentation is an important method that is widely used in the diagnosis and treatment of many diseases, as well as counting and identifying the cell nucleus. The main challenges when using this method are heterogeneous image intensities in the image, overlapping of cell nuclei, and noise. In order to overcome these difficulties, a hybrid segmentation model with attention block, CellSegUNet, is proposed, inspired by the advantageous points of UNet++ and Residual UNet models. With the proposed attention mechanism, semantic gaps that may occur are prevented by evaluating both horizontal and vertical features together. The serial and parallel connection of the convolutional blocks in the residual modules in the CellSegUNet model prevents data loss. Thus, features with stronger representation ability were obtained. The output layer, which is, especially proposed for the CellSegUNet model, calculated the differences between the data in each layer and the data in the input layer. The output value obtained from the layer level where the lowest value comes from constitutes the output of the whole system. At the same depth level, CellSegUNet versus UNet++ and ResUNet models were compared on Data Science Bowl (DSB), Sartorius Cell Instance Segmentation (SCIS), and Blood Cell Segmentation (BCS) datasets. With the CellSegUNet model, accuracy, dice, and jaccard metrics were obtained as 0.980, 0.970, 0.959 for the DSB dataset, 0.931, 0.957, 0.829 for the SCIS dataset and 0.976, 0.971, 0.927 for the BCS dataset, respectively. As a result, it is predicted that the proposed model can provide solutions to different segmentation problems.

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S5Utis: Structured State-Space Sequence SegNeXt UNet-like Tongue Image Segmentation in Traditional Chinese Medicine

Song,

Zhang,

Shi

et al. 2024

Sensors

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Intelligent Traditional Chinese Medicine can provide people with a convenient way to participate in daily health care. The ease of acceptance of Traditional Chinese Medicine is also a major advantage in promoting health management. In Traditional Chinese Medicine, tongue imaging is an important step in the examination process. The segmentation and processing of the tongue image directly affects the results of intelligent Traditional Chinese Medicine diagnosis. As intelligent Traditional Chinese Medicine continues to develop, remote diagnosis and patient participation will play important roles. Smartphone sensor cameras can provide irreplaceable data collection capabilities in enhancing interaction in smart Traditional Chinese Medicine. However, these factors lead to differences in the size and quality of the captured images due to factors such as differences in shooting equipment, professionalism of the photographer, and the subject’s cooperation. Most current tongue image segmentation algorithms are based on data collected by professional tongue diagnosis instruments in standard environments, and are not able to demonstrate the tongue image segmentation effect in complex environments. Therefore, we propose a segmentation algorithm for tongue images collected in complex multi-device and multi-user environments. We use convolutional attention and extend state space models to the 2D environment in the encoder. Then, cross-layer connection fusion is used in the decoder part to fuse shallow texture and deep semantic features. Through segmentation experiments on tongue image datasets collected by patients and doctors in real-world settings, our algorithm significantly improves segmentation performance and accuracy.

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Comparative Review on Traditional and Deep Learning Methods for Medical Image Segmentation

Cited by 3 publications

References 61 publications

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

CellSegUNet: an improved deep segmentation model for the cell segmentation based on UNet++ and residual UNet models

S5Utis: Structured State-Space Sequence SegNeXt UNet-like Tongue Image Segmentation in Traditional Chinese Medicine

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