Bone Segmentation from X-Ray Images: Challenges and Techniques

Shah, Rutvi; Sharma, Priyanka

doi:10.1007/978-981-10-7512-4_84

Cited by 5 publications

(2 citation statements)

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“…The Bone X-Ray challenges: Analyzing different bone segmentation problems in X-ray images requires studying certain bone image characteristics. X-ray images differ from other methods of medical image processing because it contains areas partially covered by various organs such as flesh, tissues or muscles [10]. Noise: A digital X-ray detector with an avg.…”

Section: Table I List Of Review Articles On Medical Image Processingmentioning

confidence: 99%

Recognition of X-rays bones: Challenges in the Past, Present and Future

Pandey¹,

Soam²,

Tripathi³

2019

IJITEE

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Image processing tool is a key role in medical uses, by extracting anatomical features the contouring of the regions from medical images. The discovery of bone sketch from X-ray images has recently introduced a new dimension in the literature as it proves to be an important step of radiological imaging analysis. X-ray images are segmented to study bone skeleton where the image is split into several parts, to examine the fracture of bones, and to plan for treatment before surgery. This report's, purpose is to survey digital image splitting methods. In image segmentation, research analysis is important for categories and it is important to provide an overview of assistive segmentation techniques with highlighting advantage and disadvantage.

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Section: Table I List Of Review Articles On Medical Image Processingmentioning

confidence: 99%

Recognition of X-rays bones: Challenges in the Past, Present and Future

Pandey¹,

Soam²,

Tripathi³

2019

IJITEE

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“…Image segmentation is one of the most important yet, challenging tasks in medical image analysis (Shah and Sharma, 2018 ). In recent years, Deep Learning's (DL) emergence has made it possible to build models that achieve human-like or superior performance in many medical imaging tasks, such as segmentation.…”

Section: Introductionmentioning

confidence: 99%

Active learning for data efficient semantic segmentation of canine bones in radiographs

Silva

Gonçalves

Franco-Gonçalo

et al. 2022

Front. Artif. Intell.

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X-ray bone semantic segmentation is one crucial task in medical imaging. Due to deep learning's emergence, it was possible to build high-precision models. However, these models require a large quantity of annotated data. Furthermore, semantic segmentation requires pixel-wise labeling, thus being a highly time-consuming task. In the case of hip joints, there is still a need for increased anatomic knowledge due to the intrinsic nature of the femur and acetabulum. Active learning aims to maximize the model's performance with the least possible amount of data. In this work, we propose and compare the use of different queries, including uncertainty and diversity-based queries. Our results show that the proposed methods permit state-of-the-art performance using only 81.02% of the data, with O(1) time complexity.

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