Using Fuzzy Inference system for detection the edges of Musculoskeletal Ultrasound Images

Jabbar, Shaima Ibraheem; Day, Charles; Chadwick, Edward K.

doi:10.1109/fuzz-ieee.2019.8858971

Cited by 6 publications

(5 citation statements)

References 25 publications

(29 reference statements)

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“…Moreover, it could exploit this technique in the enhancement of other biomedical imaging applications such as video and panoramic imaging. It is promising for use as a pre-processing step for many computer vision applications such as segmentation [21,22] and [23] tracking [24] and 3D image reconstruction [25]: particularly, in the case of musculoskeletal ultrasound imaging.…”

Section: Discussionmentioning

confidence: 99%

A new method of contrast enhancement of musculoskeletal ultrasound imaging based on fuzzy inference technique

Jabbar¹,

Aladi²,

Day³

et al. 2021

Biomed. Phys. Eng. Express

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Improving the clarity and visual quality of Musculoskeletal Ultrasound Images (MUI) can help clinicians to detect diseases more easily and accurately. In this work, we described how to enhance the contrast of MUI locally based on a fuzzy inference system. Local Fuzzy Inference Technique (LFIT) was introduced as a novel technique to enhance the contrast of MUI. The input data used musculoskeletal ultrasound images were collected from healthy volunteers. Local Fuzzy Inference Technique (LFIT) was compared with a recent fuzzy technique of the image enhancement and validated based on assessment metrics (second-derivative-like measure of enhancement (SDME)). The results advocated an improved quality of the musculoskeletal ultrasound images based on the LFIT technique with approximately 11% greater than recent technique and computation time of LFIT is 28.4% is less. It is possible to apply a proposal technique on the other types of image (panoramic image and video). Furthermore, observed improvements on the MUI quality could potentially invested as a pre-processing step before performing other computer vision applications, such as image segmentation, tracking, and 3D image reconstruction.

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Section: Discussionmentioning

confidence: 99%

A new method of contrast enhancement of musculoskeletal ultrasound imaging based on fuzzy inference technique

Jabbar¹,

Aladi²,

Day³

et al. 2021

Biomed. Phys. Eng. Express

Self Cite

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“…A state of art fuzzy edge detection method has been presented previously and provides a powerful tool to delineate the required details of muscles and tendons [15].…”

Section: Related Workmentioning

confidence: 99%

“…There are three main steps that constitute the fuzzy edge detection technique [15]. The first step is fuzzification; the key to success of this step depends on efficacious parameters selection of the fuzzy membership functions.…”

Section: Fuzzy Edge Detection Techniquementioning

confidence: 99%

“…In this step, an anisotropic diffusion filter approach [17,18] was used to reduce the speckle noise and enhance the appearance of the images. Our state of art edge detection method based on an artificial intelligence technique (fuzzy inference technique) [15] was applied in this work to extract the necessary image pixel information for evaluating the morphological parameters. Furthermore, a common image processing technique such as active contour-based segmentation [19] was utilized to obtain the final shape of an object in the image.…”

Section: Automatic Measurementmentioning

confidence: 99%

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Automated measurements of morphological parameters of muscles and tendons

Jabbar

Day

Chadwick

2021

Biomed. Phys. Eng. Express

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Capturing accurate representations of musculoskeletal system morphology is a core aspect of musculoskeletal modelling of the upper limb. Measurements of important geometric parameters such as the thickness of muscles and tendons are key descriptors of the underlying morphology. Though the measurement of those parameters can be estimated manually using cadaveric measurements, this is not an appropriate technique for constructing a personalised musculoskeletal model for an individual. Therefore, this work proposes and applies a novel method for evaluating the geometric parameters of the upper extremity based on automated ultrasound image analysis. The proposed algorithm involves advanced techniques from artificial intelligence and image processing to outline the necessary details of the musculoskeletal morphology from appropriately enhanced ultrasound images. The ultrasound images were collected from 25 healthy volunteers from different parts of upper limb. The results were compared with measurements of a manual evaluation. Our results showed that the average discrepancy between the manual and automatic measures of triceps thickness is 0.115 mm. This represents improved accuracy compared to several current approaches.

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“…The advanced fuzzy edge detector was developed to improve the selection of images where the border is blurred [ 10 , 11 ]. Fuzzy edge detection is also applied in medical image processing to resolve the vagueness that is usually found on the edge of medical images, such as retinal blood vessel detection [ 12 ] or musculoskeletal ultrasound images [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Novel Three-Dimensional Bladder Reconstruction Model from B-Mode Ultrasound Image to Improve the Accuracy of Bladder Volume Measurement

Chang

Liu

et al. 2021

Sensors

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Traditional bladder volume measurement from B-mode (two-dimensional) ultrasound has been found to produce inaccurate results, and thus in this work we aim to improve the accuracy of measurement from B-mode ultrasound. A total of 75 electronic medical records including ultrasonic images were reviewed retrospectively from 64 patients. We put forward a novel bladder volume measurement method, in which a three-dimensional (3D) reconstruction model was established from conventional two-dimensional (2D) ultrasonic images to estimate the bladder volume. The differences and relationships were analyzed among the actual volume, the traditional estimated volume, and the new reconstruction model estimated volume. We also compared the data in different volume groups from small volume to high volume. The mean actual volume is 531.8 mL and the standard deviation is 268.7 mL; the mean percentage error of traditional estimation is −28%. In our new bladder measurement method, the mean percentage error is −10.18% (N = 2), −4.72% (N = 3), −0.33% (N = 4), and 2.58% (N = 5). There is no significant difference between the actual volume and our new bladder measurement method (N = 4) in all data or the divided four groups. The estimated volumes from the traditional method or our new method are highly correlated with the actual volume. Our data show that the three-dimensional bladder reconstruction model provides an accurate measurement from conventional B-mode ultrasonic images compared with the traditional method. The accuracy is seen across different groups of volume, and thus we can conclude that this is a reliable and economical volume measurement model that can be applied in general software or in apps on mobile devices.

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Using Fuzzy Inference system for detection the edges of Musculoskeletal Ultrasound Images

Cited by 6 publications

References 25 publications

A new method of contrast enhancement of musculoskeletal ultrasound imaging based on fuzzy inference technique

A new method of contrast enhancement of musculoskeletal ultrasound imaging based on fuzzy inference technique

Automated measurements of morphological parameters of muscles and tendons

Novel Three-Dimensional Bladder Reconstruction Model from B-Mode Ultrasound Image to Improve the Accuracy of Bladder Volume Measurement

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