Image Feature Localization by Multiple Hypothesis Testing of Gabor Features

Ilonen, Jarmo; Kämäräinen, Joni-Kristian; Paalanen, P.; Hamouz, M.; Kittler, Josef; Kälviäinen, Heikki

doi:10.1109/tip.2007.916052

Cited by 42 publications

(18 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To analyze texture, we used multiple algorithms that have been successfully used in the literature: Gabor filters, 48,49 Haralick features, [50][51][52] and wavelets decomposition. 53 Gabor filters are known for representing texture in a similar way to the human vision system, 54,55 and present multiple advantages for texture extraction and classification.…”

Section: A3 Texture Featuresmentioning

confidence: 99%

Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma

et al. 2015

View full text Add to dashboard Cite

Abstract. The purpose of this study is to investigate the utility of obtaining "core samples" of regions in CT volume scans for extraction of radiomic features. We asked four readers to outline tumors in three representative slices from each phase of multiphasic liver CT images taken from 29 patients (1128 segmentations) with hepatocellular carcinoma. Core samples were obtained by automatically tracing the maximal circle inscribed in the outlines. Image features describing the intensity, texture, shape, and margin were used to describe the segmented lesion. We calculated the intraclass correlation between the features extracted from the readers' segmentations and their core samples to characterize robustness to segmentation between readers, and between human-based segmentation and core sampling. We conclude that despite the high interreader variability in manually delineating the tumor (average overlap of 43% across all readers), certain features such as intensity and texture features are robust to segmentation. More importantly, this same subset of features can be obtained from the core samples, providing as much information as detailed segmentation while being simpler and faster to obtain.

show abstract

Section: A3 Texture Featuresmentioning

confidence: 99%

Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma

et al. 2015

View full text Add to dashboard Cite

show abstract

“…We construct a set of Gabor filters G(f, θ) [17,5] for orientation parameters G(f, θ) and frequency parameters f ∈ {k, 2k, ..., 8k} where k is selected to be 1/16 to produce filter within one period. With the inclusion of the filter for the parameter (0,0) we obtain 65 different Gabor filters.…”

Section: Gabor Filter Featuresmentioning

confidence: 99%

Automated Colorectal Cancer Diagnosis for Whole-Slice Histopathology

Kalkan

Nap

Duin

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

View full text Add to dashboard Cite

Abstract. In this study, we propose a computational diagnosis system for detecting the colorectal cancer from histopathological slices.The computational analysis was usually performed on patch level where only a small part of the slice is covered. However, slice-based classification is more realistic for histopathological diagnosis. The developed method combines both textural and structural features from patch images and proposes a two level classification scheme. In the first level, the patches in slices are classified into possible classes (adenomatous, inflamed, cancer and normal) and the distribution of the patches into these classes is considered as the information representing the slices. Then the slices are classified using a logistic linear classifier. In patch level, we obtain the correct classification accuracies of 94.36% and 96.34% for the cancer and normal classes, respectively. However, in slice level, the accuracies of the 79.17% and 92.68% are achieved for cancer and normal classes, respectively.

show abstract

“…Therefore, public watermarks are not secure to use and useful for carrying IPR information. To the labels they are good alternative [5].…”

Section: Public Watermarkmentioning

confidence: 99%

GLCM and PCA Algorithm based Watermarking Scheme

Juneja¹,

Chopra²

2018

IJCA

View full text Add to dashboard Cite

Digital watermarking is the mechanism by which security is provided to the sensitive data which is stored in the databases in the form images. In this process, all the essential features of an image is extracted and calculated after which this original image is implanted into the watermark image. In this research paper, GLCM and PCA algorithm has been utilized in order to improve the working capability of the neural network based watermarking technique. Therefore, the features of the original images are extracted with the help of GLCM and PCA algorithm. The scaling factor defines the output of the PCA algorithm which is used for implementation. On the basis of simulation results it is concluded that proposed algorithm performs well in terms of PSRN and MSE and MATLAB tool is used for the implementation of the proposed method.

show abstract

Image Feature Localization by Multiple Hypothesis Testing of Gabor Features

Cited by 42 publications

References 31 publications

Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma

Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma

Automated Colorectal Cancer Diagnosis for Whole-Slice Histopathology

GLCM and PCA Algorithm based Watermarking Scheme

Contact Info

Product

Resources

About