Computer aided decision support system for cervical cancer classification

Rahmadwati, Rahmadwati; Naghdy, Golshah; Ros, Montserrat; Todd, Catherine

doi:10.1117/12.931284

Cited by 9 publications

(8 citation statements)

References 32 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However these features were subjective and hence it was difficult to use them in a consistent and reproducible manner for CIN grade classification [15]. One of the recent works applied a decision tree classifier upon morphological features extracted from the nuclei for CIN grade classification [16]. The nuclei were segmented from the epithelium using a K-means clustering and graph-cut segmentation method.…”

Section: Introductionmentioning

confidence: 99%

A fusion-based approach for uterine cervical cancer histology image classification

Stanley

et al. 2013

Computerized Medical Imaging and Graphics

View full text Add to dashboard Cite

Expert pathologists commonly perform visual interpretation of histology slides for cervix tissue abnormality diagnosis. We investigated an automated, localized, fusion-based approach for cervix histology image analysis for squamous epithelium classification into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN). The epithelium image analysis approach includes medial axis determination, vertical segment partitioning as medial axis orthogonal cuts, individual vertical segment feature extraction and classification, and image-based classification using a voting scheme fusing the vertical segment CIN grades. Results using 61 images showed at least 15.5% CIN exact grade classification improvement using the localized vertical segment fusion versus global image features.

show abstract

Section: Introductionmentioning

confidence: 99%

A fusion-based approach for uterine cervical cancer histology image classification

Stanley

et al. 2013

Computerized Medical Imaging and Graphics

View full text Add to dashboard Cite

show abstract

“…These two findings indicate that nuclei in high CIN cases attain different and irregular shapes, and these parameters also vary significantly amongst high CIN cases. Shape irregularity has been also reported in [ 15 , 16 ] (analyzing Pap smears) and [ 9 ] (quantifying nuclear features from histopathology images), as well as in [ 5 ]. Increased nuclear shape irregularity and great variation in nucleus irregularity amongst high CIN cases found in the present study reflect the fact that, in high CIN cases, nuclear atypia is dominant.…”

Section: Discussionmentioning

confidence: 89%

“…To assist the diagnosis of preinvasive lesions of the cervix, a number of computer-assisted decision support systems (DSSs) have been developed. Several of those studies have employed the biopsy material stained with H&E [ 1 , 2 , 4 , 5 , 9 – 11 ] or Feulgen stain [ 12 , 13 ] for designing DSSs. They did so by quantifying features from digital microscopy images and employing classification schemes.…”

Section: Introductionmentioning

confidence: 99%

“…Huang et al [ 15 ] and Chen et al [ 16 ] have analyzed cell images from Pap smears and have found that dysplastic cells differ from normal cells in size, nuclear proportion, nuclear shape irregularity, chromatin density, and nuclear coarseness. Rahmadwati et al [ 9 ] have analyzed histopathology images of the biopsy material of the cervix. They have found statistical significant differences between normal and abnormal cells, regarding 4 nucleus features (N/C, shape factor, compactness, and diameter).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multifeature Quantification of Nuclear Properties from Images of H&E-Stained Biopsy Material for Investigating Changes in Nuclear Structure with Advancing CIN Grade

Konstandinou

Glotsos

Kostopoulos

et al. 2018

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Background Cervical dysplasia is a precancerous condition, and if left untreated, it may lead to cervical cancer, which is the second most common cancer in women. The purpose of this study was to investigate differences in nuclear properties of the H&E-stained biopsy material between low CIN and high CIN cases and associate those properties with the CIN grade. Methods The clinical material comprised hematoxylin and eosin- (H&E-) stained biopsy specimens from lesions of 44 patients diagnosed with cervical intraepithelial neoplasia (CIN). Four or five nonoverlapping microscopy images were digitized from each patient's H&E specimens, from regions indicated by the expert physician. Sixty-three textural and morphological nuclear features were generated for each patient's images. The Wilcoxon statistical test and the point biserial correlation were used to estimate each feature's discriminatory power between low CIN and high CIN cases and its correlation with the advancing CIN grade, respectively. Results Statistical analysis showed 19 features that quantify nuclear shape, size, and texture and sustain statistically significant differences between low CIN and high CIN cases. These findings revealed that nuclei in high CIN cases, as compared to nuclei in low CIN cases, have more irregular shape, are larger in size, are coarser in texture, contain higher edges, have higher local contrast, are more inhomogeneous, and comprise structures of different intensities. Conclusion A systematic statistical analysis of nucleus features, quantified from the H&E-stained biopsy material, showed that there are significant differences in the shape, size, and texture of nuclei between low CIN and high CIN cases.

show abstract

“…Three different classes are used to label each of these images: normal, low-grade cancerous, and high-grade cancerous. 2 The training set contains 510 normal, 859 low-grade cancerous, and 275 high-grade cancerous images of 129 patients. The test set contains 491 normal, 844 low-grade cancerous, and 257 high-grade cancerous images of the remaining 129 patients.…”

Section: A Datasetmentioning

confidence: 99%

Local Object Patterns for the Representation and Classification of Colon Tissue Images

Olgun

Sökmensüer

Gündüz-Demir

2014

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

Abstract-This paper presents a new approach for the effective representation and classification of images of histopathological colon tissues stained with hematoxylin and eosin. In this approach, we propose to decompose a tissue image into its histological components and introduce a set of new texture descriptors, which we call local object patterns, on these components to model their composition within a tissue. We define these descriptors using the idea of local binary patterns, which quantify a pixel by constructing a binary string based on relative intensities of its neighbors. However, as opposed to pixel-level local binary patterns, we define our local object pattern descriptors at the component level to quantify a component. To this end, we specify neighborhoods with different locality ranges and encode spatial arrangements of the components within the specified local neighborhoods by generating strings. We then extract our texture descriptors from these strings to characterize histological components and construct the bag-of-words representation of an image from the characterized components. Working on microscopic images of colon tissues, our experiments reveal that the use of these component-level texture descriptors results in higher classification accuracies than the previous textural approaches.

show abstract

Computer aided decision support system for cervical cancer classification

Cited by 9 publications

References 32 publications

A fusion-based approach for uterine cervical cancer histology image classification

A fusion-based approach for uterine cervical cancer histology image classification

Multifeature Quantification of Nuclear Properties from Images of H&E-Stained Biopsy Material for Investigating Changes in Nuclear Structure with Advancing CIN Grade

Local Object Patterns for the Representation and Classification of Colon Tissue Images

Contact Info

Product

Resources

About