Contour evolution method for precise boundary delineation  of medical images

Natalia, Friska; Meidia, Hira; Afriliana, Nunik; Young, Julio Christian; Sudirman, Sud

doi:10.12928/telkomnika.v18i3.14746

Cited by 4 publications

(7 citation statements)

References 21 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are not many works done on UI based BIOS validation or BIOS testing procedures automation can be found in any scholarly article [13], [21]. On the other hand there are many works done on UI automation in general where some of them are applicable in BIOS validation [22]. UI automation approach can be divided into two aspects that are SUT-dependent and Host-dependent.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Advanced UI test automation (AUTA) for BIOS validation using OpenCV and OCR

Mohammed

Mustapa

Rahim

et al. 2021

IJEECS

View full text Add to dashboard Cite

<p>Basic input output system (BIOS) validation is performed on both graphical user interface (GUI) and command-line interface (CLI) by a test engineer. Keyboard and mouse are used to insert test cases commands into system under test (SUT). Test engineer monitors test cases progress on a monitor for validation. This method is time-consuming and relatively more expensive than automation. In this project we designed an independent automation system that able to mimic human interaction in BIOS validation. The approach can be divided into two main parts. The first part is the input device to enter commands into SUT and the second part is the advanced image recognizer. The keyboard and mouse emulator is used as an input device to reproduce test commands and send them to an SUT. The image analyzer algorithm is developed using OpenCV and optical character recognizer (OCR) tools to help automate some test challenges. Our result shows that advanced user interface (UI) test automation (AUTA) can perform a 125 test cases within 5 hours compared to 48 hours for a human to complete the job.</p>

show abstract

Section: The Proposed Methodsmentioning

confidence: 99%

Advanced UI test automation (AUTA) for BIOS validation using OpenCV and OCR

Mohammed

Mustapa

Rahim

et al. 2021

IJEECS

View full text Add to dashboard Cite

show abstract

“…These boundaries are important because they are used to locate certain points or landmarks from which the AP diameter and foraminal widths can be measured. Since the accuracy of locating these boundaries directly affect the accuracy of the AP diameter and foraminal widths measurements, we decide to improve the accuracy of the label images along these boundaries using a contour evolution technique [ 21 ]. These improved label images and their corresponding composite images are then used to train the SegNet model.…”

Section: Methodsmentioning

confidence: 99%

“…We have, however, previously shown [ 21 , 22 ] two drawbacks in existing active contour models. Firstly, existing approaches cannot apply contour evolution to only specific parts of the contour.…”

Section: Background and Related Workmentioning

confidence: 99%

Automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosis

et al. 2020

Self Cite

View full text Add to dashboard Cite

Lumbar Spinal Stenosis causes low back pain through pressures exerted on the spinal nerves. This can be verified by measuring the anteroposterior diameter and foraminal widths of the patient’s lumbar spine. Our goal is to develop a novel strategy for assessing the extent of Lumbar Spinal Stenosis by automatically calculating these distances from the patient’s lumbar spine MRI. Our method starts with a semantic segmentation of T1- and T2-weighted composite axial MRI images using SegNet that partitions the image into six regions of interest. They consist of three main regions-of-interest, namely the Intervertebral Disc, Posterior Element, and Thecal Sac, and three auxiliary regions-of-interest that includes the Area between Anterior and Posterior elements. A novel contour evolution algorithm is then applied to improve the accuracy of the segmentation results along important region boundaries. Nine anatomical landmarks on the image are located by delineating the region boundaries found in the segmented image before the anteroposterior diameter and foraminal widths can be measured. The performance of the proposed algorithm was evaluated through a set of experiments on the Lumbar Spine MRI dataset containing MRI studies of 515 patients. These experiments compare the performance of our contour evolution algorithm with the Geodesic Active Contour and Chan-Vese methods over 22 different setups. We found that our method works best when our contour evolution algorithm is applied to improve the accuracy of both the label images used to train the SegNet model and the automatically segmented image. The average error of the calculated right and left foraminal distances relative to their expert-measured distances are 0.28 mm ( p = 0.92) and 0.29 mm ( p = 0.97), respectively. The average error of the calculated anteroposterior diameter relative to their expert-measured diameter is 0.90 mm ( p = 0.92). The method also achieves 96.7% agreement with an expert opinion on determining the severity of the Intervertebral Disc herniations.

show abstract

“…Automated segmentation saves a lot of time, but manual landmarks annotation is still time‐consuming. Natalia et al 7,14 employed a SegNet 15 to segment the spine image and used a contour evolution technique 16 to refine the important boundaries (i.e., boundaries in red and blue in Figure 1b). Finally, landmarks were automatically detected based on a well‐designed rule and spine indices were calculated for LSS diagnosis.…”

Section: Related Workmentioning

confidence: 99%

“…The segmentation path consists of a segmentation encoder and a decoder. The regression path is composed of a regression encoder and a fully connected (FC) layer spine image and used a contour evolution technique 16 to refine the important boundaries (i.e., boundaries in red and blue in Figure 1b). Finally, landmarks were automatically detected based on a well-designed rule and spine indices were calculated for LSS diagnosis.…”

Section: Segmentation-based Spine Indices Measurementmentioning

confidence: 99%

Automated measurement of spine indices on axial MR images for lumbar spinal stenosis diagnosis using segmentation‐guided regression network

Pang

Lin

et al. 2022

Medical Physics

View full text Add to dashboard Cite

Purpose Automated measurement of spine indices on axial magnetic resonance (MR) images plays a significant role in lumbar spinal stenosis diagnosis. Existing direct spine indices measurement approaches fail to explicitly focus on the task‐specific region or feature channel with the additional information for guiding. We aim to achieve accurate spine indices measurement by introducing the guidance of the segmentation task. Methods In this paper, we propose a segmentation‐guided regression network (SGRNet) to achieve automated spine indices measurement. SGRNet consists of a segmentation path for generating the spine segmentation prediction and a regression path for producing spine indices estimation. The segmentation path is a U‐Net‐like network which includes a segmentation encoder and a decoder which generates multilevel segmentation features and segmentation prediction. The proposed segmentation‐guided attention module (SGAM) in the regression encoder extracts the attention‐aware regression feature under the guidance of the segmentation feature. Based on the attention‐aware regression feature, a fully connected layer is utilized to output the accurate spine indices estimation. Results Experiments on the open‐access Lumbar Spine MRI data set show that SGRNet achieves state‐of‐the‐art performance with a mean absolute error of 0.49 mm and mean Pearson correlation coefficient of 0.956 for four indices estimation. Conclusions The proposed SGAM in SGRNet is capable of improving the performance of spine indices measurement by focusing on the task‐specific region and feature channel under the guidance of the segmentation task.

show abstract

Contour evolution method for precise boundary delineation of medical images

Cited by 4 publications

References 21 publications

Advanced UI test automation (AUTA) for BIOS validation using OpenCV and OCR

Advanced UI test automation (AUTA) for BIOS validation using OpenCV and OCR

Automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosis

Automated measurement of spine indices on axial MR images for lumbar spinal stenosis diagnosis using segmentation‐guided regression network

Contact Info

Product

Resources

About