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1.
IEEE Trans Image Process ; 24(2): 694-708, 2015 Feb.
Article in English | MEDLINE | ID: mdl-25561593

ABSTRACT

A test for isotropy of images modeled as stationary or intrinsically stationary random fields on a lattice is developed. The test is based on the wavelet theory, and can operate on the horizontal and vertical scale of choice, or on any combination of scales. Scale is introduced through the wavelet variances (sometimes called as the wavelet power spectrum), which decompose the variance over different horizontal and vertical spatial scales. The method is more general than existing tests for isotropy, since it handles intrinsically stationary random fields as well as second-order stationary fields. The performance of the method is demonstrated on samples from different random fields, and compared with three existing methods. It is competitive with or outperforms existing methods since it consistently rejects close to the nominal level for isotropic fields while having a rejection rate for anisotropic fields comparable with the existing methods in the stationary case, and superior in the intrinsic case. As practical examples, paper density images of handsheets and mammogram images are analyzed.


Subject(s)
Algorithms , Image Processing, Computer-Assisted/methods , Wavelet Analysis , Anisotropy , Humans , Mammography
2.
Biom J ; 56(3): 363-82, 2014 May.
Article in English | MEDLINE | ID: mdl-24843881

ABSTRACT

Globalization and increased mobility of individuals enable person-to-person transmitted infectious diseases to spread faster to distant places around the world, making good models for the spread increasingly important. We study the spatiotemporal pattern of spread in the remotely located and sparsely populated region of North Norway in various models with fixed, seasonal, and random effects. The models are applied to influenza A counts using data from positive microbiology laboratory tests as proxy for the underlying disease incidence. Human travel patterns with local air, road, and sea traffic data are incorporated as well as power law approximations thereof, both with quasi-Poisson regression and based on the adjacency structure of the relevant municipalities. We investigate model extensions using information about the proportion of positive laboratory tests, data on immigration from outside North Norway and by connecting population to the movement network. Furthermore, we perform two separate analyses for nonadults and adults as children are an important driver for influenza A. Comparisons of one-step-ahead predictions generally yield better or comparable results using power law approximations.


Subject(s)
Biometry/methods , Communicable Diseases/transmission , Models, Statistical , Adolescent , Adult , Aged , Aged, 80 and over , Air Travel , Child , Child, Preschool , Communicable Diseases/epidemiology , Emigration and Immigration , Humans , Infant , Infant, Newborn , Influenza A Virus, H1N1 Subtype/physiology , Influenza A Virus, H3N2 Subtype/physiology , Influenza, Human/epidemiology , Influenza, Human/transmission , Middle Aged , Norway/epidemiology , Transportation , Young Adult
3.
Artif Intell Med ; 60(1): 13-26, 2014 Jan.
Article in English | MEDLINE | ID: mdl-24382424

ABSTRACT

BACKGROUND: It is often difficult to differentiate early melanomas from benign melanocytic nevi even by expert dermatologists, and the task is even more challenging for primary care physicians untrained in dermatology and dermoscopy. A computer system can provide an objective and quantitative evaluation of skin lesions, reducing subjectivity in the diagnosis. OBJECTIVE: Our objective is to make a low-cost computer aided diagnostic tool applicable in primary care based on a consumer grade camera with attached dermatoscope, and compare its performance to that of experienced dermatologists. METHODS AND MATERIALS: We propose several new image-derived features computed from automatically segmented dermoscopic pictures. These are related to the asymmetry, color, border, geometry, and texture of skin lesions. The diagnostic accuracy of the system is compared with that of three dermatologists. RESULTS: With a data set of 206 skin lesions, 169 benign and 37 melanomas, the classifier was able to provide competitive sensitivity (86%) and specificity (52%) scores compared with the sensitivity (85%) and specificity (48%) of the most accurate dermatologist using only dermoscopic images. CONCLUSION: We show that simple statistical classifiers can be trained to provide a recommendation on whether a pigmented skin lesion requires biopsy to exclude skin cancer with a performance that is comparable to and exceeds that of experienced dermatologists.


Subject(s)
Dermoscopy/methods , Melanoma/diagnosis , Skin Neoplasms/diagnosis , Skin Pigmentation , Humans
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