Efficient tumor volume measurement and segmentation approach for CT image based on twin support vector machines

Sathish, K.; Narayana, Y.V.; Mekala, M. S.; Patan, Rizwan; Kalyanasundaram, Suresh

doi:10.1007/s00521-021-06769-y

Cited by 3 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, there is a need to develop an inspection algorithm to identify defective 3D film products. Quality inspection for 3D films can be performed using existing research methods [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification of a 3D Film Pattern Image Using the Optimal Height of the Histogram for Quality Inspection

et al. 2023

View full text Add to dashboard Cite

A 3D film pattern image was recently developed for marketing purposes, and an inspection method is needed to evaluate the quality of the pattern for mass production. However, due to its recent development, there are limited methods to inspect the 3D film pattern. The good pattern in the 3D film has a clear outline and high contrast, while the bad pattern has a blurry outline and low contrast. Due to these characteristics, it is challenging to examine the quality of the 3D film pattern. In this paper, we propose a simple algorithm that classifies the 3D film pattern as either good or bad by using the height of the histograms. Despite its simplicity, the proposed method can accurately and quickly inspect the 3D film pattern. In the experimental results, the proposed method achieved 99.09% classification accuracy with a computation time of 6.64 s, demonstrating better performance than existing algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%

“…In addition, support vector machines (SVM) can be utilized as a method for classifying the presence or absence of defects in the 3D film [ 14 , 15 , 16 , 17 , 18 ]. Salman et al published a study in which SVM was performed after applying Canny edge detection to identify and classify leaves [ 14 ].…”

Section: Introductionmentioning

confidence: 99%