Fractal dimension and image statistics of anal intraepithelial neoplasia

Ahammer, Helmut; Kroepfl, Julia; Hackl, Ch.; Sedivy, Roland

doi:10.1016/j.chaos.2010.12.004

Cited by 18 publications

(17 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors of [3] claimed that D F reflected AIN grades well, but the mean recall actually was <0.5, excluding AIN0. Here, we included AIN0 and could not confirm that D F properly represents AIN grades.…”

Section: Discussionmentioning

confidence: 99%

“…Section 2.1): (I) all features, (II) only the statistical features, (III) only D F (D F showed statistically significant differences for AIN1-AIN3 in [3]). Hence, nine classifiers were evaluated, which are identified as Ia-IIIc.…”

Section: Learning Ain Grading Classifiersmentioning

confidence: 99%

“…We used a set of hematoxylin and eosin stained images (n = 136, 749 × 580 pixels) of different AIN grades [3,6], cf. Figure 1.…”

Section: Histopathology Datasetmentioning

confidence: 99%

“…In order to add significant value to a diagnostic process in terms of reducing time-consuming tasks, a classifier must be able to deal with a great input variance [7], generalize well, and reliably predict the grading of new images. Two crucial aspects have not been addressed in [3,4,6]: Firstly, none of the previous methods involved building a classifier on training data and evaluating it on novel test data. Secondly, since authors reported errors on training sets, we cannot assess the ability of their methods to generalize to unseen data.…”

Section: Introductionmentioning

confidence: 99%

“…Previous work used fractal image analysis [3,4] to grade AIN, or cervical intraepithelial neoplasia [5]. Fractal dimensions were computed globally for each image, and the value range was segmented by rigidly searching for statistically significant thresholds to separate the classes.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Learning discriminative classification models for grading anal intraepithelial neoplasia

Kainz

Mayrhofer-Reinhartshuber

Sedivy

et al. 2016

Current Directions in Biomedical Engineering

View full text Add to dashboard Cite

Grading intraepithelial neoplasia is crucial to derive an accurate estimate of pre-cancerous stages and is currently performed by pathologists assessing histopathological images. Inter-and intra-observer variability can significantly be reduced, when reliable, quantitative image analysis is introduced into diagnostic processes. On a challenging dataset, we evaluated the potential of learning a classifier to grade anal intraepitelial neoplasia. Support vector machines were trained on images represented by fractal and statistical features. We show that pursuing a learning-based grading strategy yields highly reliable results. Compared to existing methods, the proposed method outperformed them by a significant margin.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Learning Ain Grading Classifiersmentioning

confidence: 99%

“…We used a set of hematoxylin and eosin stained images (n = 136, 749 × 580 pixels) of different AIN grades [3,6], cf. Figure 1.…”

Section: Histopathology Datasetmentioning

confidence: 99%