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Hyperspectral imaging for diagnosis and detection of ex-vivo breast cancer.

Abstract：

BACKGROUND AND PURPOSE:Breast cancer is one of the most widely recognized tumors. .Diagnosis made in the early stage of disease may imporve outcomes. The discovery of malignant growth utilizing noninvasive light intrusive methods in lieu of conventional excisional biopsy may assist in achieving this goal. MATERIALS AND METHODS:The change of the optical properties of ex-vivo breast tissues provides different responses to light transmission, absorption, and particularly the reflection over the spectrum range. We offer the use of Hyperspectral imaging (HSI) with advanced image processing and pattern recognition in order to analyze HSI data for breast cancer detection. The spectral signatures were mined and evaluated in both malignant and normal tissue. K-mean clustering was designed for classifying hyperspectral data in order to evaluate and detection of cancer tissue. This method was used to detect ex-vivo breast cancer. Spatial spectral images were created to high spot the differences in the reflectance properties of malignant versus normal tissue. RESULTS:Trials showed that the superficial spectral reflection images within 500 nm wavelength showed high variance (214.65) between cancerous and normal breast tissues. On the other hand, image within 620 nm wavelength showed low variance (0.0020).However, the superimposed of spectral region 420-620 nm was proposed as the optimum bandwidth. Finally, the proposed HS imaging system was capable to discriminate the tumor region from normal tissue of the ex-vivo breast sample with sensitivity and a specificity of 95 % and 96 %. CONCLUSIONS:High sensitivity and specificity were achieved, which proposes potential for HSI as an edge evaluation method to enhance the surgical outcome compared to the presently available techniques in the clinics.

journal_name

Photodiagnosis Photodyn Ther

journal_title

Photodiagnosis and photodynamic therapy

authors

Aboughaleb IH,Aref MH,El-Sharkawy YH

doi

10.1016/j.pdpdt.2020.101922

subject

Has Abstract

pub_date

2020-09-01 00:00:00

pages

101922

eissn

1572-1000

issn

1873-1597

pii

S1572-1000(20)30276-3

journal_volume

31

pub_type

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