Fuzzy rule base classifier fusion for protein mass spectra based ovarian cancer diagnosis

Assareh, Amin; Volkert, L. Gwenn

doi:10.1109/cibcb.2009.4925728

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…The conjunctive, disjunctive and compromise properties of the fuzzy logic have been widely explored in image processing and have proved to be useful in image fusion. The fuzzy logic is applied both as a feature transform operator or a decision operator for image fusion [47,51,48,52,53,49,54,55,50,56,41,57,58,60,88,59,87,89,90,91,43,82,92]. There are several applications of fuzzy logic base image fusion such as brain diagnosis [47,48,49,50], cancer treatment [51], image segmentation and integration [51,52], maximization mutual information [53], deep brain stimulation [54], brain tumor segmentation [55], image retrieval [56,57], spatial weighted entropy [56], feature fusion [56], multimodal image fusion [41,58,59], ovarian cancer diagnosis [60], sensor fusion [88], natural computing methods [87] and gene expression [89,…”

Section: Methods Based On Fuzzy Logicmentioning

confidence: 99%

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Medical image fusion: A survey of the state of the art

2014

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Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

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Section: Methods Based On Fuzzy Logicmentioning

confidence: 99%

“…On pelvis, image fusion methods are used for gynecological cancer diagnosis [190,322] and analysis of conformal pelvic irradiation [236]. Ovarian cancer diagnosis uses fuzzy rule base classifier fusion [60]. The use of secondary data to estimate instantaneous model parameters of diabetic heart disease is explained in [323].…”

Section: Other Organsmentioning

confidence: 99%

Medical image fusion: A survey of the state of the art

2014

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“…This pattern has then been used to classify an independent set of 50 samples with ovarian cancer and 66 samples unaffected by ovarian cancer. In [ 44 ], a fuzzy rule based classifier fusion is proposed for feature selection and classification (diagnosis) of protein mass spectra based ovarian cancer. Demonstrated accuracy of 98-99% has been estimated through 10 ten-fold cross-validations (as opposed to 100 two-fold cross-validations used here).…”

Section: Resultsmentioning

confidence: 99%

A mixture model with a reference-based automatic selection of components for disease classification from protein and/or gene expression levels

Kopriva

Filipović

2011

BMC Bioinformatics

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BackgroundBioinformatics data analysis is often using linear mixture model representing samples as additive mixture of components. Properly constrained blind matrix factorization methods extract those components using mixture samples only. However, automatic selection of extracted components to be retained for classification analysis remains an open issue.ResultsThe method proposed here is applied to well-studied protein and genomic datasets of ovarian, prostate and colon cancers to extract components for disease prediction. It achieves average sensitivities of: 96.2 (sd = 2.7%), 97.6% (sd = 2.8%) and 90.8% (sd = 5.5%) and average specificities of: 93.6% (sd = 4.1%), 99% (sd = 2.2%) and 79.4% (sd = 9.8%) in 100 independent two-fold cross-validations.ConclusionsWe propose an additive mixture model of a sample for feature extraction using, in principle, sparseness constrained factorization on a sample-by-sample basis. As opposed to that, existing methods factorize complete dataset simultaneously. The sample model is composed of a reference sample representing control and/or case (disease) groups and a test sample. Each sample is decomposed into two or more components that are selected automatically (without using label information) as control specific, case specific and not differentially expressed (neutral). The number of components is determined by cross-validation. Automatic assignment of features (m/z ratios or genes) to particular component is based on thresholds estimated from each sample directly. Due to the locality of decomposition, the strength of the expression of each feature across the samples can vary. Yet, they will still be allocated to the related disease and/or control specific component. Since label information is not used in the selection process, case and control specific components can be used for classification. That is not the case with standard factorization methods. Moreover, the component selected by proposed method as disease specific can be interpreted as a sub-mode and retained for further analysis to identify potential biomarkers. As opposed to standard matrix factorization methods this can be achieved on a sample (experiment)-by-sample basis. Postulating one or more components with indifferent features enables their removal from disease and control specific components on a sample-by-sample basis. This yields selected components with reduced complexity and generally, it increases prediction accuracy.

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“…On pelvis gynecological imaging, fusion techniques have been utilized for cancer diagnosis [19,20]. In ovarian cancer diagnosis, fuzzy-rule-based classifier has been used for fusion [21].…”

Section: Introductionmentioning

confidence: 99%

Computer-aided Lymph Node Detection using Pelvic Magnetic Resonance Imaging

Bnouni

Rekik

Rhim

et al. 2020

IJCDS

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Pelvic Lymph Nodes (PLNs) segmentation and classification are fundamental tools in the medical image analysis of pelvic gynecological cancer such as endometrial and cervical cancer. Often used by the radiologist, PLN classification requires detailed knowledge of the morphological features of PLNs, derived from size, shape, contour and heterogeneous appearance. Accurate PLN segmentation is an essential step in PLN classification. In order to supply the best assessment of a nodal status, semiautomatic and automatic PLN segmentation and classification methods are highly desired as they can strongly capture the wide variability in morphological features and reduce classification errors due to the inter and intra-observer variability, while avoiding the time-consuming for manual delineation of PLN boundary. Nevertheless, semi-automatic segmentation methods require the clinician intervention to select the initial seed point. However, typical semi-automatic PLN segmentation methods might fail due to (1) the intensity inhomogeneity, noise and low contrast in medical images, and (2) the position of the starting point. Thus, the performance of these methods can be enhanced by using a preprocessing-based iterative segmentation approach. Currently, Magnetic Resonance Imaging (MRI) is the most common imaging modality used for staging endometrial and cervical cancer, evaluation of PLN involvement and selection of therapeutic strategy. PLN detection using classic features can be challenging due to the similarity between normal and abnormal PLNs structures. In pelvic cancer and metastatic PLN, Diffusion Weighted (DW)-MRI exhibits brighter areas indicating tumor and metastatic PLN. This paper combines anatomic T2-Weighted (T2-w) imaging with DW imaging. Specifically, we propose a computer-aided pelvic framework, which leverages (1) an ensemble preprocessing method to improve PLN segmentation, (2) the iterative correction of the position of the initial point by executing the segmentation algorithm several times in succession, (3) the fusion of structural and diffusion MRI and, (4) the extraction of morphological features of segmented PLNs (axial T2-w image) as well as intensity feature derived from the fused image for the final classification of PLNs as suspect or non-suspect. Research in the field of PLN detection is important as it can help doctors to better detect cervical and endometrial cancer and decide the appropriate treatment. To the best of our knowledge, this is the first work to segment and classify PLN. Our preprocessing-based iterative segmentation approach significantly (p<0.05) improved comparison segmentation methods, with a segmentation accuracy boosted from 61.37% for the conventional region-growing algorithm to 66.53% for the proposed method. Furthermore, we obtained an average accuracy of 78.50% for pelvic nodule classification.

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Fuzzy rule base classifier fusion for protein mass spectra based ovarian cancer diagnosis

Cited by 9 publications

References 17 publications

Medical image fusion: A survey of the state of the art

Medical image fusion: A survey of the state of the art

A mixture model with a reference-based automatic selection of components for disease classification from protein and/or gene expression levels

Computer-aided Lymph Node Detection using Pelvic Magnetic Resonance Imaging

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