Computer technology in detection and staging of prostate carcinoma: A review

Zhu, Yanong; Williams, S. M.; Zwiggelaar, Reyer

doi:10.1016/j.media.2005.06.003

Cited by 66 publications

(43 citation statements)

References 116 publications

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“…Each image can be classified into an appropriate grade by using Bayesian, k-NN, and support vector machine (SVM) classifiers, respectively. A review of computer technologies in detection and staging of prostate carcinoma can be found in [8].…”

Section: Related Workmentioning

confidence: 99%

Automatic detection of malignant prostatic gland units in cross-sectional microscopic images

Hua

2010

2010 IEEE International Conference on Image Processing

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Prostate cancer is the second most frequent cause of cancer deaths among men in the US. In the most reliable screening method, histological images from a biopsy are examined under a microscope by pathologists. In an early stage of prostate cancer, only relatively few gland units in a large region become malignant. Discovering such sparse malignant gland units using a microscope is a laborintensive and error-prone task for pathologists. In this paper, we develop effective image segmentation and classification methods for automatic detection of malignant gland units in microscopic images. Both segmentation and classification methods are based on carefully designed feature descriptors, including color histograms and texton co-occurrence tables.

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Section: Related Workmentioning

confidence: 99%

Automatic detection of malignant prostatic gland units in cross-sectional microscopic images

Hua

2010

2010 IEEE International Conference on Image Processing

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“…This is the only technique that so far allows examination of both the underlying tissue and cell architectures, not visible under any other medical image modality (Zhu et al, 2006). The art of microscopic examination is a complex combination of different skills that physicians develop along their training periods, during which the know-how is achieved by exposing students to the analysis of as many cases as possible.…”

Section: Ntroductionmentioning

confidence: 99%

Micro‐structural tissue analysis for automatic histopathological image annotation

Díaz

Romero

2011

Microscopy Res & Technique

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This article presents a new approach for extracting high level semantic concepts from digital histopathological images. This strategy provides not only annotation of several biological concepts, but also a coarse location of these concepts. The proposed approach is composed of five main steps: (1) a stain decomposition stage, which separates the contribution of hematoxylin and eosin dyes, (2) a color standardization that corrects color image differences, (3) a part-based representation, which describes the image in terms of the conditional probability of relevant local patches, selected by their stain contributions, (4) a discriminative classification model, which bridges out the found patterns and the biological concepts, (5) a block-based annotation strategy that identifies the multiple biological concepts within an image. A set of 655 skin images, containing 10 biological concepts of skin tissues were used for assessing the proposed approach, obtaining a sensitivity of 84% and a specificity of 67% when annotating images with multiple concepts.

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“…Step 3, refine a subset of the features which suits the classification problem; Step 4, use the selected feature set to classify examples based on appropriate supervised or unsupervised classifiers (Vince et al, 2000;Smutek et al, 2001;Lu et al, 2003;Zhu et al, 2006;Chrzanowski et al, 2008). Chen et al(2005) distinguished solid malignant from benign breast lesions using fractal textural features.…”

Section: Ultrasound Image Recognitionmentioning

confidence: 99%

A fast automatic recognition and location algorithm for fetal genital organs in ultrasound images

Tang

Chen

2009

J. Zhejiang Univ. Sci. B

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Abstract:Severe sex ratio imbalance at birth is now becoming an important issue in several Asian countries. Its leading immediate cause is prenatal sex-selective abortion following illegal sex identification by ultrasound scanning. In this paper, a fast automatic recognition and location algorithm for fetal genital organs is proposed as an effective method to help prevent ultrasound technicians from unethically and illegally identifying the sex of the fetus. This automatic recognition algorithm can be divided into two stages. In the 'rough' stage, a few pixels in the image, which are likely to represent the genital organs, are automatically chosen as points of interest (POIs) according to certain salient characteristics of fetal genital organs. In the 'fine' stage, a specifically supervised learning framework, which fuses an effective feature data preprocessing mechanism into the multiple classifier architecture, is applied to every POI. The basic classifiers in the framework are selected from three widely used classifiers: radial basis function network, backpropagation network, and support vector machine. The classification results of all the POIs are then synthesized to determine whether the fetal genital organ is present in the image, and to locate the genital organ within the positive image. Experiments were designed and carried out based on an image dataset comprising 658 positive images (images with fetal genital organs) and 500 negative images (images without fetal genital organs). The experimental results showed true positive (TP) and true negative (TN) results from 80.5% (265 from 329) and 83.0% (415 from 500) of samples, respectively. The average computation time was 453 ms per image.

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Computer technology in detection and staging of prostate carcinoma: A review

Cited by 66 publications

References 116 publications

Automatic detection of malignant prostatic gland units in cross-sectional microscopic images

Automatic detection of malignant prostatic gland units in cross-sectional microscopic images

Micro‐structural tissue analysis for automatic histopathological image annotation

A fast automatic recognition and location algorithm for fetal genital organs in ultrasound images

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