An approach for an automatic fracture detection of skull dicom images based on neighboring pixels

Abubacker, Nirase Fathima; Azman, Azreen; Azrifah, Masrah; Doraisamy, Shyamala

doi:10.1109/isda.2013.6920731

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…However, as the gradient magnitude drops, the accuracy almost surely diminishes. Abubacker et al (2013) provided a simple and fast automatic approach in Digital Imaging and Communications in Medicine (DICOM) to extract the skull bone and diagnose the fracture utilizing histogram-based thresholding and neighboring pixel connection search. This approach's experimental results are consistent, with a high detection rate.…”

Section: Related Workmentioning

confidence: 99%

Classifications of Skull Fractures using CT Scan Images via CNN with Lazy Learning Approach

Emon¹,

Ornob²,

Rahman³

2022

Journal of Computer Science

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Classification of skull fracture is a challenging task for both radiologists and researchers. Skull fractures result in broken pieces of bone, which can cut into the brain and cause bleeding and other injury types. So it is vital to detect and classify the fracture very early. In real world, often fractures occur at multiple sites. This makes it harder to detect the fracture type where many fracture types might summarize a skull fracture. Unfortunately, manual detection of skull fracture and the classification process is time-consuming, threatening a patient's life. Because of the emergence of deep learning, this process could be automated. Convolutional Neural Networks (CNNs) are the most widely used deep learning models for image categorization because they deliver high accuracy and outstanding outcomes compared to other models. We propose a new model called SkullNetV1 comprising a novel CNN by taking advantage of CNN for feature extraction and lazy learning approach which acts as a classifier for classification of skull fractures from brain CT images to classify five fracture types. Our suggested model achieved a subset accuracy of 88%, an F1 score of 93%, the Area Under the Curve (AUC) of 0.89 to 0.98, a Hamming score of 92% and a Hamming loss of 0.04 for this seven-class multi-labeled classification.

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Section: Related Workmentioning

confidence: 99%

Classifications of Skull Fractures using CT Scan Images via CNN with Lazy Learning Approach

Emon¹,

Ornob²,

Rahman³

2022

Journal of Computer Science

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“…Later, the number of skeleton endpoints was applied for SFD by the same research team 5 . Abubacker et al 6 . detected skull fractures by the disconnectedness of adjacent pixels.…”

Section: Introductionmentioning

confidence: 99%

“…Later, the number of skeleton endpoints was applied for SFD by the same research team. 5 Abubacker et al 6 detected skull fractures by the disconnectedness of adjacent pixels. Yamada et al 7 leveraged a black-hat transformation to identify skull fractures.…”

Section: Introductionmentioning

confidence: 99%

Fracture R‐CNN: An anchor‐efficient anti‐interference framework for skull fracture detection in CT images

et al. 2022

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Background Skull fracture, as a common traumatic brain injury, can lead to multiple complications including bleeding, leaking of cerebrospinal fluid, infection, and seizures. Automatic skull fracture detection (SFD) is of great importance, especially in emergency medicine. Purpose Existing algorithms for SFD, developed based on hand‐crafted features, suffer from low detection accuracy due to poor generalizability to unseen samples. Deploying deep detectors designed for natural images like Faster Region‐based Convolutional Neural Network (R‐CNN) for SFD can be helpful but are of high redundancy and with nonnegligible false detections due to the cranial suture and skull base interference. Therefore, we, for the first time, propose an anchor‐efficient anti‐interference deep learning framework named Fracture R‐CNN for accurate SFD with low computational cost. Methods The proposed Fracture R‐CNN is developed by incorporating the prior knowledge utilized in clinical diagnosis into the original Faster R‐CNN. Specifically, based on the distributions of skull fractures, we first propose an adaptive anchoring region proposal network (AA‐RPN) to generate proposals for diverse‐scale fractures with low computational complexity. Then, based on the prior knowledge that cranial sutures exist in the junctions of bones and usually contain sclerotic margins, we design an anti‐interference head (A‐Head) network to eliminate the cranial suture interference for better SFD detection. In addition, to further enhance the anti‐interference ability of the proposed A‐Head, a difficulty‐balanced weighted loss function is proposed to emphasize more on distinguishing the interference areas from the skull base and the cranial sutures during training. Results Experimental results demonstrate that the proposed Fracture R‐CNN outperforms the current state‐of‐the‐art (SOTA) deep detectors for SFD with a higher recall and fewer false detections. Compared to Faster R‐CNN, the proposed Fracture R‐CNN improves the average precision (AP) by 11.74% and the free‐response receiver operating characteristic (FROC) score by 11.08%. Through validating on various backbones, we further demonstrate the architecture independence of Fracture R‐CNN, making it extendable to other detection applications. Conclusions As the customized deep learning–based framework for SFD, Fracture R‐CNN can effectively overcome the unique challenges in SFD with less computational cost, leading to a better detection performance compared to the SOTA deep detectors. Moreover, we believe the prior knowledge explored for Fracture R‐CNN would shed new light on future deep learning approaches for SFD.

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“…In the field of machine vision, such images and videos are heavily relied upon in transforming the information of the real-world into digital data. This digital data becomes the basis for the development of various algorithms towards the realization of tasks which include but are not limited to traffic monitoring [1], video surveillance [2], real-time target tracking [3], target recognition [4], fracture detection in medicine [5], satellite remote sensing [6] and until recently, driver-less vehicle technology [7]. It is an undeniable fact that all these machine vision algorithms rely immensely on pixel level information that needs to be guaranteed in order for these algorithms to achieve acceptable performance.…”

Section: Introductionmentioning

confidence: 99%

A Research on Single Image Dehazing Algorithms Based on Dark Channel Prior

Alharbi¹,

Ge²,

Wang³

2016

JCC

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In the field of computer and machine vision, haze and fog lead to image degradation through various degradation mechanisms including but not limited to contrast attenuation, blurring and pixel distortions. This limits the efficiency of machine vision systems such as video surveillance, target tracking and recognition. Various single image dark channel dehazing algorithms have aimed to tackle the problem of image hazing in a fast and efficient manner. Such algorithms rely upon the dark channel prior theory towards the estimation of the atmospheric light which offers itself as a crucial parameter towards dehazing. This paper studies the state-of-the-art in this area and puts forwards their strengths and weaknesses. Through experiments the efficiencies and shortcomings of these algorithms are shared. This information is essential for researchers and developers in providing a reference for the development of applications and future of the research field.

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An approach for an automatic fracture detection of skull dicom images based on neighboring pixels

Cited by 6 publications

References 6 publications

Classifications of Skull Fractures using CT Scan Images via CNN with Lazy Learning Approach

Classifications of Skull Fractures using CT Scan Images via CNN with Lazy Learning Approach

Fracture R‐CNN: An anchor‐efficient anti‐interference framework for skull fracture detection in CT images

A Research on Single Image Dehazing Algorithms Based on Dark Channel Prior

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