Abstract

Conventional multivariate strategies for making qualitative estimates of sample composition rely chiefly on identifying subtle differences in spectral shape. In some instances, such as in biological tissues, the spectra obtained from a single sample class may consist of many shapes. Likewise, two distinctly different sample classes, such as normal and abnormal tissue, may produce similar variations in spectral shape. In our work, we employ statistical analysis of the set of cosine correlation scores obtained from multispectral visible absorption images of stained cervical Papanicolaou samples. By analyzing the cosine correlation score frequency for spectra obtained from the cell nuclei, abnormal cells can be differentiated from the background of normal cells, which vary considerably in their optical properties and morphology. Our method, called cosine histogram analysis (CHA), returns the percent likelihood of abnormality for each pixel in the field of view and is presented here for the first time.

Classification accuracy improvement of laser-induced breakdown spectroscopy based on histogram of oriented gradients features of spectral images

Jiujiang Yan, Ping Yang, Zhongqi Hao, Ran Zhou, Xiangyou Li, Shisong Tang, Yun Tang, Xiaoyan Zeng, and Yongfeng Lu
Opt. Express 26(22) 28996-29004 (2018)

Image DNA-index (ploidy) analysis in cancer diagnosis

P. S. Oud, A. G. J. M. Hanselaar, M. M. M. Pahlplatz, J. W. R. Meijer, and G. Peter Vooijs
Appl. Opt. 26(16) 3349-3355 (1987)

Fluorescent image classification by major color histograms and a neural network

M. Soriano, L. Garcia, and C. Saloma
Opt. Express 8(5) 271-277 (2001)

Dynamic descriptors for contextual classification of remotely sensed hyperspectral image data analysis

Wun C. Chiou
Appl. Opt. 23(21) 3889-3892 (1984)

Smartphone-based fluorescence spectroscopic device for cervical precancer diagnosis: a random forest classification of in vitro data

Shivam Shukla, Chaitanya Vishwakarma, Amar Nath Sah, Shikha Ahirwar, Kiran Pandey, and Asima Pradhan
Appl. Opt. 62(25) 6826-6834 (2023)