A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models

Using Hidden Markov Models (HMMs) as a recognition framework for automatic classification of animal vocalizations has a number of benefits, including the ability to handle duration variability through nonlinear time alignment, the ability to incorporate complex language or recognition constraints, and easy extendibility to continuous recognition and detection domains. In this work, we apply HMMs to several different species and bioacoustic tasks using generalized spectral features that can be easily adjusted across species and HMM network topologies suited to each task. This experimental work includes a simple call type classification task using one HMM per vocalization for repertoire analysis of Asian elephants, a language-constrained song recognition task using syllable models as base units for ortolan bunting vocalizations, and a stress stimulus differentiation task in poultry vocalizations using a non-sequential model via a one-state HMM with Gaussian mixtures. Results show strong performance across all tasks and illustrate the flexibility of the HMM framework for a variety of species, vocalization types, and analysis tasks

A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models

Using Hidden Markov Models (HMMs) as a recognition framework for automatic classification of animal vocalizations has a number of benefits, including the ability to handle duration variability through nonlinear time alignment, the ability to incorporate complex language or recognition constraints, and easy extendibility to continuous recognition and detection domains. In this work, we apply HMMs to several different species and bioacoustic tasks using generalized spectral features that can be easily adjusted across species and HMM network topologies suited to each task. This experimental work includes a simple call type classification task using one HMM per vocalization for repertoire analysis of Asian elephants, a language-constrained song recognition task using syllable models as base units for ortolan bunting vocalizations, and a stress stimulus differentiation task in poultry vocalizations using a non-sequential model via a one-state HMM with Gaussian mixtures. Results show strong performance across all tasks and illustrate the flexibility of the HMM framework for a variety of species, vocalization types, and analysis tasks

Application of Fractal and Wavelets in Microcalcification Detection

Breast cancer has been recognized as one or the most frequent, malignant tumors in women, clustered microcalcifications in mammogram images has been widely recognized as an early sign of breast cancer. This work is devote to review the application of Fractal and Wavelets in microcalcifications detection

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed

Suunatud ja ülegenoomsel sekveneerimisel põhinevate mitteinvasiivsete sünnieelsete testide arvutusmeetodite ja töövoogude väljatöötamine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneLoote sõeluuring võimaldab avastada lootel esinevaid arenguhäireid ja sagedasemaid kromosoomhaiguseid, nagu näiteks Down’i, Edwards’i ja Patau sündroom. Varajane teave lootel esineva kromosoomhaiguse kohta võimaldab langetada informeeritud otsust raseduse jätkamise osas ning aitab tulevasi vanemaid paremini ette valmistada. Tavapärane loote sõeluuring sisaldab loote ultraheli uuringut ja vereseerumi analüüsi, mille abil tuvastatakse enamik kromosoomhaigusega loodetest. Lõpliku diagnoosi saamiseks suunatakse kõrge riski saanud patsient edasi invasiivsele protseduurile. Eelnimetatud sõeluuringute puuduseks on arvestatav valepositiivsete hulk, mistõttu enamik positiivse testitulemuse saanud patsientidest kannab täiesti tervet loodet. Sõeluuringule järgnev invasiivne protseduur on neil juhtudel ebavajalik, põhjustab rasedatele asjatut stressi ning sellega võib kaasneda suurenenud oht raseduse katkemiseks. Antud doktoritöö keskseks teemaks on mitte-invasiivne sünnieelne testimine (NIPT), mis põhineb ema veres leiduva loote päritolu rakuvaba DNA analüüsil. Võrreldes eelmainitud traditsionaalsete sõeluuringu meetoditega, on NIPT oluliselt sensitiivsem ja spetsiifilisem sagedamini esinevate kromosoomihäirete avastamiseks. Doktoritöö raames arendati välja TAC-seq põhine analüüsi töövoog, mida rakendati 21. kromosoom trisoomia tuvastamiseks. Lisaks töötati välja NIPT analüüsiraamistik, mis kasutab erinevaid masinõppe metoodikaid loote trisoomia määramiseks rakuvaba DNA-st. Niisamuti viidi Eesti rasedate kohordil läbi NIPT metoodika validatsiooni uuring, milles rakendati ülegenoomsel sekveneerimisel põhinevat töövoogu sagedamate loote kromosoomihäirete määramiseks. Üldiselt on nii suunatud kui ka ülegenoomsel NIPT meetoditel muutnud rasedate sõeluuring varasemast veel täpsemaks. Kui suunatud sekveneerimise suureks eeliseks on kulutõhusus, siis ülegenoomne lähenemine tuvastab valimatult kõikvõimalikke geneetilisi aberratsioone üle kogu genoomi.Fetal screening allows to detect congenital anomalies and more frequent chromosomal abnormalities, such as Down, Edwards and Patau syndrome. Early information about a fetus’s possible health problem allows to make an informed decision about the continuation of the pregnancy and better prepare the future parents. Conventional screening includes an ultrasound and blood serum analysis by way of which most of the fetal chromosomal abnormalities are detected. For a final diagnosis, the patients who are deemed to have a high risk for fetal chromosomal aberrations are referred to an invasive procedure. The disadvantage of the aforementioned screening method is a considerable number of false positive results, which is why most of the patients who receive a positive result are actually carrying a fully healthy fetus. The invasive procedure that follows the screening is unnecessary for those patients, causes them undue stress and this may also lead to a higher risk of miscarriage. The focal point of this doctoral thesis is non-invasive prenatal testing (NIPT), which is based on the analysis of cell-free DNA (cfDNA) of fetal origin that is found in maternal blood. In comparison to the above-mentioned conventional screening methods, NIPT is considerably more sensitive and specific for detecting the most common chromosomal abnormalities. In the framework of the thesis, TAC-seq based analysis workflow was developed and used to detect chromosome 21 trisomy. In addition, NIPT analysis framework, which uses different machine learning methods, was developed for determining fetal trisomies from cfDNA sample. Also, a validation study of NIPT was carried out on pregnant women in Estonian cohort using a whole-genome sequencing based workflow. In general, both targeted and whole-genome sequencing based NIPT methods have made prenatal screening of fetal aneuplodies even more accurate than before. While cost-effectiveness is a major advantage of the targeted sequencing based approach, the whole-genome sequencing based NIPT possibly detects all kinds of genetic aberrations across the genome.https://www.ester.ee/record=b549777

Joint Segmentation and Deconvolution of Ultrasound Images Using a Hierarchical Bayesian Model Based on GeneralizedGaussian Priors

International audienceThis paper proposes a joint segmentation and deconvolution Bayesian method for medical ultrasound (US) images. Contrary to piecewise homogeneous images, US images exhibit heavy characteristic speckle patterns correlated with the tissue structures. The generalized Gaussian distribution (GGD) has been shown to be one of the most relevant distributions for characterizing the speckle in US images. Thus, we propose a GGD-Potts model defined by a label map coupling US image segmentation and deconvolution. The Bayesian estimators of the unknown model parameters, including the US image, the label map, and all the hyperparameters are difficult to be expressed in a closed form. Thus, we investigate a Gibbs sampler to generate samples distributed according to the posterior of interest. These generated samples are finally used to compute the Bayesian estimators of the unknown parameters. The performance of the proposed Bayesian model is compared with the existing approaches via several experiments conducted on realistic synthetic data and in vivo US images