Endometriosis Laparoscopic Image Reconstruction Using PCA and IPCA

S Visalaxi, T. Sudalaimuthu, Varadharaj Krithika
In: 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) · 2021 · vol. 9 , pp. 1–4 · doi:10.1109/icaeca52838.2021.9675620 · W4206409634
article OA: closed CC0 ⤵ 1 in-corpus citation
View on OpenAlex View at publisher

Abstract

Principle component analysis is a “state-of-art” technique for handling medical-based image data. The Laparoscopic images of Endometriosis are in raw format and large volumes and hence the images cannot be used directly for further processing. The Principal Component Analysis (PCA) has been used to handle such a large volume of images without affecting pixel intensity. A algorithm has been proposed using PCA & IPCA to identify the exact components needed to compress the images. The size of the image has been reduced by implementing PCA Eigenvectors and Eigenvalues. The best component value identified through IPCA is 300 where the variance is 95 %.

My notes (saved in your browser only)

Condition tags

endometriosis

Citation neighborhood (sparse)

Too few in-corpus citations on either side for a chart; here are the lists.

Cites (2)

Cited by (1)

References (17)

Cited by (1)

Source provenance

openalex
last seen: 2026-06-04T00:00:01.174412+00:00
unpaywall
last seen: 2026-06-02T02:00:03.124865+00:00
License: CC0 · commercial use OK