Comparison of Edge Detection Algorithms for Automated Radiographic Measurement of the Carrying Angle

AlNouri, Mason; Saei, Jasim Al; Younis, Manaf H; Bouri, Fadi; Habash, Mohamed Ali Al; Shah, Mohammed Hamza; Dosari, Mohammed Al Ateeq Al

doi:10.14738/jbemi.26.1753

Cited by 2 publications

(2 citation statements)

References 45 publications

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“…For this reason, in order to assess the contours or bounds (complexity) of the images, all of them were transformed to the monochrome gray color and the employed software allowed us to analyze the texture and projections simultaneously [ 7 ]. There exist several operators to calculate the object contours [ 8 ], and the most frequently used operators such as Sobel, Prewitt, Scharr, Arzela were tested by us ( Figure 2 ).…”

Section: Methodsmentioning

confidence: 99%

Radiomics in Breast Cancer: In-Depth Machine Analysis of MR Images of Metastatic Spine Lesion

Steinhauer¹,

Sergeev²

2022

Sovrem Tehnol Med

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Using mathematic criteria for image processing (radiomics) makes it possible to more accurately assess the nature of therapy-associated changes and determine the sites of maximal response. Comparison of the acquired quantitative and clinical data may assist radiologists in making the optimal decision. The aim of the study was to assess the capabilities of software operators for an in-depth analysis of metastatic spine lesion images in breast cancer. Materials and Methods MRI data of three patients with breast cancer T 2 N 2–3 M 1 receiving treatment in accordance with the accepted clinical protocols were used in our work. Spinal metastases were assessed by a radiologist and machine analysis using the Arzela variation operators. Twelve MRI examinations (4 per each patient) excluding the baseline examination have been analyzed with a follow-up period of about 3 months. Results The structure of the metastatically modified spine was analysed segment by segment in the sagittal and axial projections using machine image analysis operators. Rapid changes in the “complexity” of vertebrae images have been found, allowing one to suggest the efficacy of treatment in one of the three options — stabilization, improvement, progression. Changes in the vertebrae structure with a positive response to the treatment in the form of the formation of bone objects, calderas, reduction of the contrast agent circulation at the microlevel, confirmed by mathematical analysis, have been monitored. A correlation was obtained between the established changes and the level of the CA 15-3 cancer marker. Conclusion The study has shown a high effectiveness of machine image analysis algorithms, high correlation of the obtained results with the radiologist’s report and clinical and laboratory data in 9 cases out of 12. The Pearson correlation coefficient between the classical marker and matrix filter curve was 0.8.

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Section: Methodsmentioning

confidence: 99%

Radiomics in Breast Cancer: In-Depth Machine Analysis of MR Images of Metastatic Spine Lesion

Steinhauer¹,

Sergeev²

2022

Sovrem Tehnol Med

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“…A significant issue in the field of estimating the IQ is the datasets are typically tiny, on average including less than few thousand images. The challenge is effectively solved in the current work by using the edge detector method of Scharr [25,26] to acquire the vertical and horizontal edge maps in every image and feeding them to the artificial neural network. The modified input allows extraction of higher level features of image more quickly from a smaller dataset in terms of size by make use of the convolutional layers.…”

Section: Proposed Approachmentioning

confidence: 99%

An Edge Distortion and CNN-Based Analysis of Blind IQ

Babu¹

2022

MSEA

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This paper is for assessing the image quality (IQ) without using an authentic image (original image) which is a type of Blind IQ Assessment (BIQA) model by introducing a technique of Convolutional Neural Network (CNN). The distortions of edges in the image are considered as features to represent the image feature vector. This approach is justified by the evidence that the subjective evaluation concentrates on image characteristics that radiate from the boundaries and edges that exist with in the image. It was identified in the prior methods that the features are extracted at the time of training or before training by applying sophisticated transformations on the image. In this work, the vertical along with horizontal edge feature maps of the training images are extracted by means of Scharr Kernel (SK) approach. These edge maps subsequently fed into a CNN, which uses non-linear transformations to bring out higher-level features. Regression is then used to link the generated features to the IQ score. To accommodate different sizes of input images, the SPP (Spatial Pyramid Pooling) layer is used in this network. The developed model was evaluated using well-known datasets in the field of IQA. The suggested model's performance reveals that it outperforms previously existing models in context of negligible complexity involvement and feature extraction simplicity.

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Comparison of Edge Detection Algorithms for Automated Radiographic Measurement of the Carrying Angle

Cited by 2 publications

References 45 publications

Radiomics in Breast Cancer: In-Depth Machine Analysis of MR Images of Metastatic Spine Lesion

Radiomics in Breast Cancer: In-Depth Machine Analysis of MR Images of Metastatic Spine Lesion

An Edge Distortion and CNN-Based Analysis of Blind IQ

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