Estimating Cluster Overlap on Manifolds and its Application to Neuropsychiatric Disorders

Wang, Peng; Kohler, Christian G.; Verma, Rohit

doi:10.1109/cvpr.2007.383407

Cited by 5 publications

(8 citation statements)

References 16 publications

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“…Seventeen articles were selected for inclusion in this review. These articles were published from 2007 to 2021, with the number of publications increasing over time: three completed before 2009 [32][33][34][35], four completed between 2010 to 2014 [36][37][38][39], and eight were completed subsequent to 2015 [40][41][42][43][44][45][46][47][48].…”

Section: Resultsmentioning

confidence: 99%

“…[ [33][34][35] Both Participants were asked to express happiness, anger, fear, sadness, and disgust at mild, moderate, and peak levels, respectively. In the evoked session, participants were guided through vignettes, which were provided by the participants themselves and describe a situation in their life pertaining to each emotion.…”

Section: Article Passive/ Evokedmentioning

confidence: 99%

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Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review

et al. 2022

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Background Schizophrenia is a severe psychiatric disorder that causes significant social and functional impairment. Currently, the diagnosis of schizophrenia is based on information gleaned from the patient’s self-report, what the clinician observes directly, and what the clinician gathers from collateral informants, but these elements are prone to subjectivity. Utilizing computer vision to measure facial expressions is a promising approach to adding more objectivity in the evaluation and diagnosis of schizophrenia. Method We conducted a systematic review using PubMed and Google Scholar. Relevant publications published before (including) December 2021 were identified and evaluated for inclusion. The objective was to conduct a systematic review of computer vision for facial behavior analysis in schizophrenia studies, the clinical findings, and the corresponding data processing and machine learning methods. Results Seventeen studies published between 2007 to 2021 were included, with an increasing trend in the number of publications over time. Only 14 articles used interviews to collect data, of which different combinations of passive to evoked, unstructured to structured interviews were used. Various types of hardware were adopted and different types of visual data were collected. Commercial, open-access, and in-house developed models were used to recognize facial behaviors, where frame-level and subject-level features were extracted. Statistical tests and evaluation metrics varied across studies. The number of subjects ranged from 2-120, with an average of 38. Overall, facial behaviors appear to have a role in estimating diagnosis of schizophrenia and psychotic symptoms. When studies were evaluated with a quality assessment checklist, most had a low reporting quality. Conclusion Despite the rapid development of computer vision techniques, there are relatively few studies that have applied this technology to schizophrenia research. There was considerable variation in the clinical paradigm and analytic techniques used. Further research is needed to identify and develop standardized practices, which will help to promote further advances in the field.

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Section: Resultsmentioning

confidence: 99%

Section: Article Passive/ Evokedmentioning

confidence: 99%

Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review

et al. 2022

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“…The facial responses are collected as a video and analyzed to determine special features associated with depression, with accuracies from 76.7% up to 79.0% [161], [162]. In addition to depression, a similar protocol has previously been applied in a study on schizophrenia by Wang et al [163], demonstrating the inability of schizophrenic patients to express different emotions. Eventually, thermal imaging has been applied to assess facial thermal changes from induced emotion to classify moderate and high schizophrenia patients [164].…”

Section: Neurologic Conditions and Neurodevelopmental Disordersmentioning

confidence: 99%

“…facial morphology abnormalities / characteristics Down syndrome 1 [11], [115], [119]- [122] disturbances in facial expressions depression 2 [161], [162] schizophrenia 4 [15] ADHD 2, 5 [141] acromegaly 1 [180], [181] autism 2,5 [141] craniofacial deformations 1,4 [13], [136] schizophrenia 2,3 [163], [164] fetal alcohol exposure 1, 4 [12], [123]- [131], [135], [136] abnormal heat in orbital area Graves' ophthalmopathy 3 [26], [185] Cornelia de Lange syndrome 1 [116], [133], [134] nonspecific condition (Ophtalmology) 3 [186] Cushing's syndrome 1 [181], [182] other syndromes 1, 4 [117], [133], [134], [136], [137] asymmetry + synkinesis facial nerve dysfunction 1, 2 [86], [88], [89], [104], [105] facial asymmetry facial paralysis 1,5 [83]- [85], [97]- [100],…”

Section: Facial Symptoms Possible Conditions References Facial Symptomentioning

confidence: 99%

A Survey on Computer Vision for Assistive Medical Diagnosis From Faces

Thevenot

López

Hadid

2018

IEEE J. Biomed. Health Inform.

136

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Automatic medical diagnosis is an emerging center of interest in computer vision as it provides unobtrusive objective information on a patient's condition. The face, as a mirror of health status, can reveal symptomatic indications of specific diseases. Thus, the detection of facial abnormalities or atypical features is at upmost importance when it comes to medical diagnostics. This survey aims to give an overview of the recent developments in medical diagnostics from facial images based on computer vision methods. Various approaches have been considered to assess facial symptoms and to eventually provide further help to the practitioners. However, the developed tools are still seldom used in clinical practice, since their reliability is still a concern due to the lack of clinical validation of the methodologies and their inadequate applicability. Nonetheless, efforts are being made to provide robust solutions suitable for healthcare environments, by dealing with practical issues such as real-time assessment or patients positioning. This survey provides an updated collection of the most relevant and innovative solutions in facial images analysis. The findings show that with the help of computer vision methods, over 30 medical conditions can be preliminarily diagnosed from the automatic detection of some of their symptoms. Furthermore, future perspectives, such as the need for interdisciplinary collaboration and collecting publicly available databases, are highlighted.

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“…In our previous work [9], we have presented a probabilistic method to estimate Bayes error between different clusters as the measurement of their overlap, using a k-NN/Parzen window estimation of the Bayes error. Unlike other methods of k-NN/Parzen window estimation, we define the nearest neighbors as obtained from the manifold learning, which directly estimates the posterior probability of clusters, and automatically selects the parameter of the window function for cluster overlap estimation.…”

Section: Cluster Overlapmentioning

confidence: 99%

On Detecting Subtle Pathology via Tissue Clustering of Multi-parametric Data using Affinity Propagation

Verma

Wang

2007

2007 IEEE 11th International Conference on Computer Vision

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We propose a novel framework for tissue abnormality characterization in normal appearing brain tissue (NABT) that is progressively deteriorating, using affinity propagation applied to multi-parametric data created using a combination of Magnetic Resonance (MR) protocols. While traditional tissue segmentation and clustering can reveal clusters pertaining to healthy and diseased tissue easily, a complete characterization of the effect of pathology requires the study of heterogeneity of NABT. The problem is rendered challenging by the fact that there are no training samples available for such tissue and hence classification based techniques cannot be used and neither can traditional clustering techniques since the number of clusters are not known a priori. Our framework for the automated clustering of tissue types employs a combination of a) manifold learning, that determines the underlying non-linear structure and embeds it into a lower dimensional space and b) affinity propagation (AP), which is a novel clustering technique that combines model-and similarity-based clustering, to automatically obtain exemplar-based clustering. We also define a novel probabilistic clustering technique. The number of clusters associated with a tissue type is indicative of its heterogeneity. By computing the overlap of these clusters in each of the MR protocols, we obtain a measure of the degree of abnormality in a tissue type and the protocol most sensitive in providing that classification.This general framework is applied towards the characterization of NABT in patients with multiple sclerosis. Results demonstrate a greater heterogeneity in NABT surrounding the lesions along with a greater overlap between the NABT and lesion tissue.

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Estimating Cluster Overlap on Manifolds and its Application to Neuropsychiatric Disorders

Abstract: Although it is usually assumed in many pattern recognition

Cited by 5 publications

References 16 publications

Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review

Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review

A Survey on Computer Vision for Assistive Medical Diagnosis From Faces

On Detecting Subtle Pathology via Tissue Clustering of Multi-parametric Data using Affinity Propagation

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