ROI Detection in Mammogram Images Using Wavelet-Based Haralick and HOG Features

Tasdemir, Sena Busra Yengec; Taşdemir, Kasım; Aydın, Zafer

doi:10.1109/icmla.2018.00023

Cited by 14 publications

(6 citation statements)

References 21 publications

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“…In this paper, we used the DT and SVM classifiers as they are commonly used in ML techniques. In addition, they proved their efficiency over the other classifiers [71] , [72] .…”

Section: Methodology and Limitationsmentioning

confidence: 91%

Urban spaces as a positive catalyst during pandemics: Assessing the community’s well-being by using artificial intelligence techniques

Salama

Ragab

Abdel-Moneim

2023

Ain Shams Engineering Journal

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“…In this paper, we used the DT and SVM classifiers as they are commonly used in ML techniques. In addition, they proved their efficiency over the other classifiers [71] , [72] .…”

Section: Methodology and Limitationsmentioning

confidence: 91%

Urban spaces as a positive catalyst during pandemics: Assessing the community’s well-being by using artificial intelligence techniques

Salama

Ragab

Abdel-Moneim

2023

Ain Shams Engineering Journal

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“…A descriptor is then constructed to form a subset of features. Once key points are selected in the scale space, these key points, being extrema in the scale space, undergo wavelet transformation [22][23][24][25] to separate multiscale features around the key points, achieving effective feature extraction. Rectangular sub-images are established with key points as centers to describe key point information using sub-region image features, enabling mutual matching between key points in two images.…”

Section: Interference Fringe Feature Extraction and Matchingmentioning

confidence: 99%

“…After removing the flat ground effect from the interference fringes, which reflects terrain undulation information with a gradually changing gradient, analyzing local gradient information around feature points can effectively describe the current feature points. Histogram of oriented gradient (HOG) is a technique used for texture-based image analysis [24][25][26], simplifying images by extracting gradient information. HOG extracts features that have a locally distinctive shape based on edges or gradient structures.…”

Section: Interference Fringe Feature Extraction and Matchingmentioning

confidence: 99%

Airborne Platform Three-Dimensional Positioning Method Based on Interferometric Synthetic Aperture Radar Interferogram Matching

Li,

Wang,

Wang

et al. 2024

Remote Sensing

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As the demand for precise navigation of aircraft increases in modern society, researching high-precision, high-autonomy navigation systems is both theoretically valuable and practically significant. Because the inertial navigation system (INS) has systematic and random errors, its output information diverges. Therefore, it is necessary to combine them with other navigation systems for real-time compensation and correction of these errors. The SAR matching positioning and navigation system uses synthetic aperture radar (SAR) image matching for platform positioning and compensates for the drift caused by errors in the inertial measurement unit (IMU). Images obtained by SAR are matched with digital landmark data, and the platform’s position is calculated based on the SAR imaging geometry. However, SAR matching positioning faces challenges due to seasonal variations in SAR images, the need for typical landmarks for matching, and the lack of elevation information in two-dimensional SAR image matching. This paper proposes an airborne platform positioning method based on interferometric SAR (InSAR) interferogram matching. InSAR interferograms contain terrain elevation information, are less affected by seasonal changes, and provide higher positioning accuracy and robustness. By matching real-time InSAR-processed interferograms with simulated interferograms using a digital elevation model (DEM), three-dimensional position information about the matching points has been obtained. Subsequently, a three-dimensional positioning model for the platform has bene established using the unit line-of-sight vector decomposition method. In actual flight experiments using an FMCW Ku-band Interferometric SAR system, the proposed platform positioning framework demonstrated its ability to achieve precise positioning in the absence of signals from the global navigation satellite system (GNSS).

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“…The performance of their model on the quin-classi cation task was not satisfactory (achieving an accuracy of 39.04%), so, in their last contribution they showed the fusion of the ROI and classi er outputs for WSI-level diagnosis helped improving accuracy. In more traditional mannerisms of extraction of features from digital mammography imaging, Yengec Tasdemir et al ( 2019) [71] detected abnormal areas in a mammography by features extracted by Histogram of Oriented Gradients (HOG) [72] and Haralick features [73] to detect ROIs for presence of BCa. The mammography was segmented into smaller ROIs of size 73×68 and then converted into a two dimensional Discrete Wavelet Transform (2D-DWT) for multi-resolution decomposition of the ROIs [74].…”

Section: Region Of Interest (Roi)-based Approachesmentioning

confidence: 99%

On the dynamics and feasibility of transferred inference for diagnosis of invasive ductal carcinoma: A perspective

Harshvardhan

Sahu

Gourisaria

et al. 2021

Preprint

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It is generally noticed that increasing the number of convolutional layers in generic image classification procedures proves to be detrimental to model performance in terms of validation accuracy and loss. Apart from vanilla CNNs, we have state-of-the-art (SOTA) architectures such as ResNet50 (and its variants) which show that through the use of skip-connections, higher performance metrics are attainable through deeper architectures. However, most evaluative metrics converge on a log scale as we go deeper with diminishing gradient of the metrics' curves. Given these two contrasting speculations, in this paper, we implement various vanilla and SOTA CNNs for the diagnosis of one of the most common forms of breast cancer - invasive ductal carcinoma (IDC) - to examine and understand the feasibility of implementation of SOTA CNNs through transferred weights when juxtaposed with vanilla CNNs (and LeNet-5) of varying configurations in terms of their performance metrics and other parameters. In this paper, we solve the dual-objective of studying behavioural aspects of avant-garde CNN models (more specifically, VGG16, VGG19, ResNet50, ResNet50V2, MobileNetV2, and DenseNet121) and proper diagnosis of IDC through intermediate neural activations to critically evaluate and theorize the performance of different models. We notice that among all the models, only VGG16, VGG19, LeNet-5 and a selected vanilla CNN through an optimization procedure were the ones to attain the best metrics, shared amongst them.

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ROI Detection in Mammogram Images Using Wavelet-Based Haralick and HOG Features

Cited by 14 publications

References 21 publications

Urban spaces as a positive catalyst during pandemics: Assessing the community’s well-being by using artificial intelligence techniques

Urban spaces as a positive catalyst during pandemics: Assessing the community’s well-being by using artificial intelligence techniques

Airborne Platform Three-Dimensional Positioning Method Based on Interferometric Synthetic Aperture Radar Interferogram Matching

On the dynamics and feasibility of transferred inference for diagnosis of invasive ductal carcinoma: A perspective

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