Multi-Faceted Hierarchical Image Segmentation Taxonomy (MFHIST)

Goswami, Tilottama; Agarwal, Arun; Chillarige, Raghavendra Rao

doi:10.1109/access.2021.3055678

Cited by 3 publications

(2 citation statements)

References 86 publications

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“…The Figure 1 depicts the comparison of the above mentioned state of art related works using the Multi-Faceted Hierarchical Image Segmentation Taxonomy (MFHIST) [11]. This will help in proper categorization and comparative study of the algorithms depending on the six facets presented in hierarchical manner -scope, requirement, control, feature, image representation and approach specifications.…”

Section: Related Workmentioning

confidence: 99%

“…Semantic segmentation or automated image segmentation is performed mostly using modern deep learning techniques based on representation learning [9], [10]. Image representation has been captured as one of main facets in Multi Faceted Image Segmentation Taxonomy [11], in which the authors have also mentioned some of the recent contributions regarding this. Good quality of image representation is good alternative to region detectors as rich metadata.…”

Section: Introductionmentioning

confidence: 99%

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Generalized image understanding of natural outdoor images is fundamentally a bottom-up approach guided by the data and information received from the digital input images. The importance of intermediate image representation that aids generalized image understanding, inspired the proposed a unified approach for providing a new meaningful and objective representation of a given outdoor scene. The proposed unified framework provides a novel set of string and semantic codes, based on inferential statistical data obtained from segments characterization and blocks characterization of natural outdoor images. The framework acts as an analytical tool that can categorize the input images into four types based on the inferential statistical representation -1) Object-based image, 2) Heterogeneous color based texture image, 3) Homogeneous color based texture image, 4) Pure toned image. The proposed string and semantic code characterization is also rotation and scale variant. Such a representation is simple, unsupervised, computationally inexpensive and also places minimal demand on the storage. Later for a higher-level image analysis and understanding the proposed unified approach can provide focused interaction between the above intermediate representation and domain scene knowledge for object detection, classification, or recognition.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

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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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