Automated breast cancer classification using near-infrared optical tomographic images

Wang, James; Liang, Xiaoping; Zhang, Qizhi; Fajardo, Laurie L.; Jiang, Huabei

doi:10.1117/1.2956662

Cited by 13 publications

(12 citation statements)

References 31 publications

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“…However, these data sets have limited spatial information and orders of magnitude fewer measurements per subject than, for example, the breast tomograms to be discussed below. Other researchers have implemented automated DOT image analysis techniques to identify lesions in a particular subject [95–97] however, this analysis neglects information about the common signatures of cancer across a population.…”

Section: Probability Of Malignancy Optical Indices Based On Diffusementioning

confidence: 99%

Toward Noninvasive Characterization of Breast Cancer and Cancer Metabolism with Diffuse Optics

et al. 2013

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We review recent developments in diffuse optical imaging and monitoring of breast cancer, i.e. optical mammography. Optical mammography permits non-invasive, safe and frequent measurement of tissue hemodynamics oxygen metabolism and components (lipids, water, etc.), the development of new compound indices indicative of the risk and malignancy, and holds potential for frequent non-invasive longitudinal monitoring of therapy progression.

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Section: Probability Of Malignancy Optical Indices Based On Diffusementioning

confidence: 99%

Toward Noninvasive Characterization of Breast Cancer and Cancer Metabolism with Diffuse Optics

et al. 2013

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“…A few researchers implemented automated image methods with DOT to identify lesions in a particular subject. [21][22][23] This per-subject analysis, however, neglects information about the common signatures of cancer across a population. Still other researchers pursued hypothesis-driven multiparameter optical metrics with DOS 24,25 and DOT.…”

Section: Ib Limitations Of Current Diffuse Optics Analysismentioning

confidence: 99%

Computer aided automatic detection of malignant lesions in diffuse optical mammography

et al. 2010

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Purpose:Computer aided detection ͑CAD͒ data analysis procedures are introduced and applied to derive composite diffuse optical tomography ͑DOT͒ signatures of malignancy in human breast tissue. In contrast to previous optical mammography analysis schemes, the new statistical approach utilizes optical property distributions across multiple subjects and across the many voxels of each subject. The methodology is tested in a population of 35 biopsy-confirmed malignant lesions. Methods: DOT CAD employs multiparameter, multivoxel, multisubject measurements to derive a simple function that transforms DOT images of tissue chromophores and scattering into a probability of malignancy tomogram. The formalism incorporates both intrasubject spatial heterogeneity and intersubject distributions of physiological properties derived from a population of cancercontaining breasts ͑the training set͒. A weighted combination of physiological parameters from the training set define a malignancy parameter ͑M͒, with the weighting factors optimized by logistic regression to separate training-set cancer voxels from training-set healthy voxels. The utility of M is examined, employing 3D DOT images from an additional subjects ͑the test set͒. Results: Initial results confirm that the automated technique can produce tomograms that distinguish healthy from malignant tissue. When compared to a gold standard tissue segmentation, this protocol produced an average true positive rate ͑sensitivity͒ of 89% and a true negative rate ͑speci-ficity͒ of 94% using an empirically chosen probability threshold. Conclusions: This study suggests that the automated multisubject, multivoxel, multiparameter statistical analysis of diffuse optical data is potentially quite useful, producing tomograms that distinguish healthy from malignant tissue. This type of data analysis may also prove useful for suppression of image artifacts.

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“…14,15 In another study, logistic regression was used in semi-automatic detection of malignant breast lesions in DOT images, 16 while a fourth study extracts attributes from three imaging parameters obtained by an NIR imaging system and employs an SVM algorithm to distinguish between malignant and benign lesions. 17 A separate effort has focused on the automated detection of contrast-to-noise ratio regions of interest for DOT imaging of breast tissue. 18,19 Other studies investigated the ability to discriminate between breast tissue malignancies using tissue fluorescence and reflectance measurements from diffuse reflectance spectroscopy of excised 20 and in vivo 21 breast tissue.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction

et al. 2013

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Abstract. This is the first part of a two-part paper on the application of computer-aided diagnosis to diffuse optical tomography (DOT). An approach for extracting heuristic features from DOT images and a method for using these features to diagnose rheumatoid arthritis (RA) are presented. Feature extraction is the focus of Part 1, while the utility of five classification algorithms is evaluated in Part 2. The framework is validated on a set of 219 DOT images of proximal interphalangeal (PIP) joints. Overall, 594 features are extracted from the absorption and scattering images of each joint. Three major findings are deduced. First, DOT images of subjects with RA are statistically different (p < 0.05) from images of subjects without RA for over 90% of the features investigated. Second, DOT images of subjects with RA that do not have detectable effusion, erosion, or synovitis (as determined by MRI and ultrasound) are statistically indistinguishable from DOT images of subjects with RA that do exhibit effusion, erosion, or synovitis. Thus, this subset of subjects may be diagnosed with RA from DOT images while they would go undetected by reviews of MRI or ultrasound images. Third, scattering coefficient images yield better one-dimensional classifiers. A total of three features yield a Youden index greater than 0.8. These findings suggest that DOT may be capable of distinguishing between PIP joints that are healthy and those affected by RA with or without effusion, erosion, or synovitis.

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Automated breast cancer classification using near-infrared optical tomographic images

Cited by 13 publications

References 31 publications

Toward Noninvasive Characterization of Breast Cancer and Cancer Metabolism with Diffuse Optics

Toward Noninvasive Characterization of Breast Cancer and Cancer Metabolism with Diffuse Optics

Computer aided automatic detection of malignant lesions in diffuse optical mammography

Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction

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