A Clinical System For Digital Imaging Colposcopy

Craine, Brian L.; Craine, E. R.; Engel, John R.; Wemple, Neil T.

doi:10.1117/12.968674

Cited by 11 publications

(9 citation statements)

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“…This is expressed succinctly by the image irradiance equation E(x,y) = R(n(x,y), I, p) (1) where 1 is the light source direction and p is the surface albedo. For a surface which is nonemissive, its radiance is equivalent to its reflectance map, which is usually expressed as a function of the surface gradient components, i.e.…”

Section: Mathematical Modelmentioning

confidence: 99%

<title>Cervical surface shape recovery using digital imaging colposcopy</title>

Engel

Craine

et al. 1994

SPIE Proceedings

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A common application of digital imaging colposcopy in cervical examinations is the measurement of lesion dimensions and areas. Typically this is done by interactively marking the region of interest on a cervix image, calculating the corresponding pixel dimensions and then scaling to the colposcope optics. Until now no one has suggested a solution to the effects of the cervical surface slant on these measurements. Away from the cervical os the surface slant is large and lesion dimensions there will be underestimated, possibly leading to a misinterpretation of the lesion's progression.In this paper we discuss a non-invasive method for determining the surface geometry of the cervix using digital imaging colposcopy. The method is an application of shape-from-shading techniques used to determine the surface slant at all points in the cervix image. From the surface slant we can calculate area corrections to measurements made on the image. In our initial investigations we have applied this method to area measurements of circular regions drawn on spherical test targets.Our results indicate that we can obtain improvements in area measurement errors of factors between 3 and 6, resulting in relative errors of a few percent.

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Section: Mathematical Modelmentioning

confidence: 99%

<title>Cervical surface shape recovery using digital imaging colposcopy</title>

Engel

Craine

et al. 1994

SPIE Proceedings

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“…According to World Health Organization (WHO) [1], every year in India alone there are 1 lakh 30 thousand women who are affected with cervical cancer. According to Chittaranjan National Cancer Research Institute's Cancer Registry Program [2], in the state of West Bengal alone, out of every 1000 women, aged between 30 years to 60 years old, 18 are affected by cervical cancer. To detect cervical cancer, the state does not possess the adequate infrastructure.…”

Section: Introductionmentioning

confidence: 99%

A Novel Analysis of Clinical Data and Image Processing Algorithms in Detection of Cervical Cancer

Das

Kar

Bhattacharyya

2015

Lecture Notes in Electrical Engineering

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Abstract. Cervical Cancer is one of the ubiquitous forms of cancer afflicting the female population worldwide. A Digital Colposcope is a self-illuminated powerful microscope which acquires the image of the affected cervix. The raw cervix image acquired by the colposcope is known as a cervigram. The raw cervigram is preprocessed by removing the specular reflections and then the region of interest is sought. Different novel segmentation algorithms which were proposed in our earlier papers are applied to the cervigram to detect the acetowhite tissues. In the current paper, we try to draw a correlation between Pap smear results, colposcopic findings, histopathologic biopsy results and our proposed algorithmic analysis. The significance of association is assessed by chi-square testing and the strength of association by kappa statistic. The association between impression and histology was significant, the strength of correlation was also significant. The overall kappa statistic for the concordance of the algorithm with the human experts was 0.809, while that among raters, excluding the algorithm was 0.804. The proposed algorithm had better agreement with the expert decision.

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“…However, the Pap test is unable to accurately and consistently identify premalignant and malignant disease of cervix. The incidence of both false-negative and falsepositive test have become shockingly high [1]. These have motivated the use of colposcopy as a standard screening procedure for precancer examination [1] and for performing accurate punch biopsies for histological analysis.…”

Section: Introductionmentioning

confidence: 99%

“…The incidence of both false-negative and falsepositive test have become shockingly high [1]. These have motivated the use of colposcopy as a standard screening procedure for precancer examination [1] and for performing accurate punch biopsies for histological analysis. One of the major factors hindering the full utilization of colposcopy is the difficulty in training physicians in the recognition of pathology.…”

Section: Introductionmentioning

confidence: 99%

Texture analysis for classification of cervix lesions

Engel

Craine

2000

IEEE Trans. Med. Imaging

126

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Abstract-This paper presents a generalized statistical texture analysis technique for characterizing and recognizing typical, diagnostically most important, vascular patterns relating to cervical lesions from colposcopic images. The contributions of the research include: 1) the introduction of a generalized texture analysis technique based on the combination of the conventional statistical and structural textural analysis approaches by using a statistical description of geometric primitives; 2) the introduction of a set of textural measures that capture the specific characteristics of cervical textures as perceived by human. Experimental study with real images demonstrated the feasibility and promising of the proposed approach in discriminating between cervical texture patterns indicative of different stages of cervical lesions.

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A Clinical System For Digital Imaging Colposcopy

Cited by 11 publications

References 0 publications

<title>Cervical surface shape recovery using digital imaging colposcopy</title>

<title>Cervical surface shape recovery using digital imaging colposcopy</title>

A Novel Analysis of Clinical Data and Image Processing Algorithms in Detection of Cervical Cancer

Texture analysis for classification of cervix lesions

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