Histology image analysis for carcinoma detection and grading

He, Lei; Long, L. Rodney; Antani, Sameer; Thoma, George R.

doi:10.1016/j.cmpb.2011.12.007

Cited by 307 publications

(197 citation statements)

References 121 publications

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“…Anyway, because of the human intellectual and visual limitations in accurate image processing, computer-based image processing with image detailed analyzing, and recognizing any abnormal tissue changes can help with early detection of cancer (Giger, 2004;Jain and Vijay, 2013;Hasanabadi et al, 2014). In recent decades, medical image processing has advanced increasingly (He et al, 2012) .…”

Section: Advances In Optimal Detection Of Cancer By Image Processing;mentioning

confidence: 99%

Advances in Optimal Detection of Cancer by Image Processing; Experience with Lung and Breast Cancers

Mohammadzadeh

Safdari²,

Ghazisaeidi³

et al. 2015

Asian Pacific Journal of Cancer Prevention

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Clinicians should looking for techniques that helps to early diagnosis of cancer, because early cancer detection is critical to increase survival and cost effectiveness of treatment, and as a result decrease mortality rate. Medical images are the most important tools to provide assistance. However, medical images have some limitations for optimal detection of some neoplasias, originating either from the imaging techniques themselves, or from human visual or intellectual capacity. Image processing techniques are allowing earlier detection of abnormalities and treatment monitoring. Because the time is a very important factor in cancer treatment, especially in cancers such as the lung and breast, imaging techniques are used to accelerate diagnosis more than with other cancers. In this paper, we outline experience in use of image processing techniques for lung and breast cancer diagnosis. Looking at the experience gained will help specialists to choose the appropriate technique for optimization of diagnosis through medical imaging.

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Section: Advances In Optimal Detection Of Cancer By Image Processing;mentioning

confidence: 99%

Advances in Optimal Detection of Cancer by Image Processing; Experience with Lung and Breast Cancers

Mohammadzadeh

Safdari²,

Ghazisaeidi³

et al. 2015

Asian Pacific Journal of Cancer Prevention

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“…Appropriate preprocessing methods could reduce variations to some degree, 42 such as color normalization to minimize staining variations, 43 spatial filtering to highlight major image structure, denoising to reduce image noise, and enhancement to optimize contrast between objects of interest and background. 5 Moreover, intensity centering and histogram equalization were presented particularly to normalize a diverse set of pathology images. Figure 2 presents the results of color normalization after applying Reinhard's method.…”

Section: Image Preprocessingmentioning

confidence: 99%

“…Usually, pathologists adopt a multi-resolution approach to reading different tissue structures in images. Accordingly, three levels of features, including pixel level, object level, and spatial-arrangement level, are generated to optimally describe tissue morphology 5,39 (Table 2). Pixel-level features that capture properties in pixel classification ( Figure 5(b)), such as color features and texture features, are the least interpretable in terms of current pathological knowledge.…”

Section: Image Preprocessingmentioning

confidence: 99%

“…2,3 Studies have demonstrated that such tools have potential to tackle the inherent subjectivity in manual analysis and to largely reduce the workload of pathologists via high-throughput analysis. 2,4,5 Hematoxylin and eosin (H&E) staining remains the most important and fundamental method for tumor histological examination in pathology. 6 Unfortunately, manual analysis of H&E images has remained an experiencebased qualitative assessment for over 100 years.…”

Section: Introductionmentioning

confidence: 99%

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Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review

Chen

et al. 2017

Tumour Biol.

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With the advance of digital pathology, image analysis has begun to show its advantages in information analysis of hematoxylin and eosin histopathology images. Generally, histological features in hematoxylin and eosin images are measured to evaluate tumor grade and prognosis for breast cancer. This review summarized recent works in image analysis of hematoxylin and eosin histopathology images for breast cancer prognosis. First, prognostic factors for breast cancer based on hematoxylin and eosin histopathology images were summarized. Then, usual procedures of image analysis for breast cancer prognosis were systematically reviewed, including image acquisition, image preprocessing, image detection and segmentation, and feature extraction. Finally, the prognostic value of image features and image feature–based prognostic models was evaluated. Moreover, we discussed the issues of current analysis, and some directions for future research.

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“…This study uses cervical histology images as input to the CADSS system. A typical CADSS consists of sequences of image processing and analysis tools including image acquisition, pre-processing, segmentation, feature extraction, classification, gradation and disease identification [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Computer aided decision support system for cervical cancer classification

et al. 2012

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Conventional analysis of a cervical histology image, such a pap smear or a biopsy sample, is performed by an expert pathologist manually. This involves inspecting the sample for cellular level abnormalities and determining the spread of the abnormalities. Cancer is graded based on the spread of the abnormal cells. This is a tedious, subjective and timeconsuming process with considerable variations in diagnosis between the experts. This paper presents a computer aided decision support system (CADSS) tool to help the pathologists in their examination of the cervical cancer biopsies. The main aim of the proposed CADSS system is to identify abnormalities and quantify cancer grading in a systematic and repeatable manner. The paper proposes three different methods which presents and compares the results using 475 images of cervical biopsies which include normal, three stages of pre cancer, and malignant cases. This paper will explore various components of an effective CADSS; image acquisition, pre-processing, segmentation, feature extraction, classification, grading and disease identification. Cervical histological images are captured using a digital microscope. The images are captured in sufficient resolution to retain enough information for effective classification. Histology images of cervical biopsies consist of three major sections; background, stroma and squamous epithelium. Most diagnostic information are contained within the epithelium region. This paper will present two levels of segmentations; global (macro) and local (micro). At the global level the squamous epithelium is separated from the background and stroma. At the local or cellular level, the nuclei and cytoplasm are segmented for further analysis. Image features that influence the pathologists' decision during the analysis and classification of a cervical biopsy are the nuclei's shape and spread; the ratio of the areas of nuclei and cytoplasm as well as the texture and spread of the abnormalities. Similar features are extracted towards the automated classification process. This paper will present various feature extraction methods including colour, shape and texture using Gabor wavelet as well as various quantative metrics. Generated features are used to classify cells or regions into normal and abnormal categories. Following the classification process, the cancer is graded based on the spread of the abnormal cells. This paper will present the results of the grading process with five stages of the cancer spectrum. ABSTRACTConventional analysis of a cervical histology image, such a pap smear or a biopsy sample, is performed by an expert pathologist manually. This involves inspecting the sample for cellular level abnormalities and determining the spread of the abnormalities. Cancer is graded based on the spread of the abnormal cells. This is a tedious, subjective and timeconsuming process with considerable variations in diagnosis between the experts. This paper presents a computer aided decision support system (CADSS) tool to help the patholo...

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Histology image analysis for carcinoma detection and grading

Cited by 307 publications

References 121 publications

Advances in Optimal Detection of Cancer by Image Processing; Experience with Lung and Breast Cancers

Advances in Optimal Detection of Cancer by Image Processing; Experience with Lung and Breast Cancers

Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review

Computer aided decision support system for cervical cancer classification

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