Key Frame Extraction Analysis Based on Optimized Convolution Neural Network (OCNN) using Intensity Feature Selection (IFS)

Prabakaran, T.; Kumar, Loveleen; Ashabharathi, S; Prabhavathi, S; Deshpande, Maneesh Vilas; Fahlevi, Mochammad

doi:10.1109/ictacs56270.2022.9988474

Cited by 2 publications

(1 citation statement)

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“…Optical flow analysis, which tracks the apparent movement of pixels between consecutive frames, is commonly used. The second type of approaches is intensity-based methods [21] analyzing pixel intensity values to identify keyframes. They often involve techniques such as histogram analysis, thresholding, and peak detection.…”

Section: Related Workmentioning

confidence: 99%

Deep AngioKey: A Novel Approach for Objective Keyframe Extraction in Coronary Angiography Analysis

Moalla,

Ghrab,

Bahloul

et al. 2023

Preprint

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Coronary angiography represents the GOLD standard for diagnosing and treating obstructive coronary artery disease. Its analysis relies on cardiologist?s visual assessment which is subjective and leads to inter-observer variability and different treatment strategies. Objective methods have been proposed and implemented in catheterization laboratories. However, their input is manually chosen by the cardi-ologist. In this paper, we present a new coronary angiogram keyframe extraction, Deep AngioKey. It is based on feature extraction. The frame with the highest vessel structure was identified as keyframe. Feature extraction was done using a U-Net model trained for binary vessel segmentation. Our solution surpassed existing methods and reached an overall accuracy of 98.1% in keyframe identification with an average main frame distance of 1.63.

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Section: Related Workmentioning

confidence: 99%