Multiple contour extraction from graylevel images using an artificial neural network

For active contour modeling (ACM), we propose a novel self-organizing map (SOM)-based approach, called the batch-SOM (BSOM), that attempts to integrate the advantages of SOM- and snake-based ACMs in order to extract the desired contours from images. We employ feature points, in the form of ail edge-map (as obtained from a standard edge-detection operation), to guide the contour (as in the case of SOM-based ACMs) along with the gradient and intensity variations in a local region to ensure that the contour does not "leak" into the object boundary in case of faulty feature points (weak or broken edges). In contrast with the snake-based ACMs, however, we do not use an explicit energy functional (based on gradient or intensity) for controlling the contour movement. We extend the BSOM to handle extraction of contours of multiple objects, by splitting a single contour into as many subcontours as the objects in the image. The BSOM and its extended version are tested on synthetic binary and gray-level images with both single and multiple objects. We also demonstrate the efficacy of the BSOM on images of objects having both convex and nonconvex boundaries. The results demonstrate the superiority of the BSOM over others. Finally, we analyze the limitations of the BSOM.

Assessment of In-House Natural Product and Synthetic Compound Libraries Based on In vitro Inhibition of Cholinesterases

The first line medication for mild to moderate Alzheimer s disease (AD) is based on cholinesterase inhibitors which prolong the effect of the neurotransmitter acetylcholine in cholinergic nerve synapses which relieves the symptoms of the disease. Implications of cholinesterases involvement in disease modifying processes has increased interest in this research area. The drug discovery and development process is a long and expensive process that takes on average 13.5 years and costs approximately 0.9 billion US dollars. Drug attritions in the clinical phases are common due to several reasons, e.g., poor bioavailability of compounds leading to low efficacy or toxic effects. Thus, improvements in the early drug discovery process are needed to create highly potent non-toxic compounds with predicted drug-like properties. Nature has been a good source for the discovery of new medicines accounting for around half of the new drugs approved to market during the last three decades. These compounds are direct isolates from the nature, their synthetic derivatives or natural mimics. Synthetic chemistry is an alternative way to produce compounds for drug discovery purposes. Both sources have pros and cons. The screening of new bioactive compounds in vitro is based on assaying compound libraries against targets. Assay set-up has to be adapted and validated for each screen to produce high quality data. Depending on the size of the library, miniaturization and automation are often requirements to reduce solvent and compound amounts and fasten the process. In this contribution, natural extract, natural pure compound and synthetic compound libraries were assessed as sources for new bioactive compounds. The libraries were screened primarily for acetylcholinesterase inhibitory effect and secondarily for butyrylcholinesterase inhibitory effect. To be able to screen the libraries, two assays were evaluated as screening tools and adapted to be compatible with special features of each library. The assays were validated to create high quality data. Cholinesterase inhibitors with various potencies and selectivity were found in natural product and synthetic compound libraries which indicates that the two sources complement each other. It is acknowledged that natural compounds differ structurally from compounds in synthetic compound libraries which further support the view of complementation especially if a high diversity of structures is the criterion for selection of compounds in a library.

FTIR-spektroskopia kasviuutteiden analysoinnissa

FTIR-spektroskopia (Fourier-muunnosinfrapunaspektroskopia) on nopea analyysimenetelmä. Fourier-laitteissa interferometrin käyttäminen mahdollistaa koko infrapunataajuusalueen mittaamisen muutamassa sekunnissa. ATR-liitännäisellä varustetun FTIR-spektrometrin käyttö ei edellytä juuri näytteen valmistusta ja siksi menetelmä on käytössä myös helppo. ATR-liitännäinen mahdollistaa myös monien erilaisten näytteiden analysoinnin. Infrapunaspektrin mittaaminen onnistuu myös sellaisista näytteistä, joille perinteisiä näytteenvalmistusmenetelmiä ei voida käyttää. FTIR-spektroskopian avulla saatu tieto yhdistetään usein tilastollisiin monimuuttuja-analyyseihin. Klusterianalyysin avulla voidaan spektreistä saatu tieto ryhmitellä samanlaisuuteen perustuen. Hierarkkisessa klusterianalyysissa objektien välinen samanlaisuus määritetään laskemalla niiden välinen etäisyys. Pääkomponenttianalyysin avulla vähennetään datan ulotteisuutta ja luodaan uusia korreloimattomia pääkomponentteja. Pääkomponenttien tulee säilyttää mahdollisimman suuri määrä alkuperäisen datan variaatiosta. FTIR-spektroskopian ja monimuuttujamenetelmien sovellusmahdollisuuksia on tutkittu paljon. Elintarviketeollisuudessa sen soveltuvuutta esimerkiksi laadun valvontaan on tutkittu. Menetelmää on käytetty myös haihtuvien öljyjen kemiallisten koostumusten tunnistukseen sekä öljykasvien kemotyyppien havaitsemiseen. Tässä tutkimuksessa arvioitiin menetelmän käyttöä suoputken uutenäytteiden luokittelussa. Tutkimuksessa suoputken eri kasvinosien uutenäytteiden FTIR-spektrejä vertailtiin valikoiduista puhdasaineista mitattuihin FTIR-spektreihin. Puhdasaineiden FTIR-spektreistä tunnistettiin niiden tyypilliset absorptiovyöhykkeet. Furanokumariinien spektrien intensiivisten vyöhykkeiden aaltolukualueet valittiin monimuuttuja-analyyseihin. Monimuuttuja-analyysit tehtiin myös IR-spektrin sormenjälkialueelta aaltolukualueelta 1785-725 cm-1. Uutenäytteitä pyrittiin luokittelemaan niiden keräyspaikan ja kumariinipitoisuuden mukaan. Keräyspaikan mukaan ryhmittymistä oli havaittavissa, mikä selittyi vyöhykkeiden aaltolukualueiden mukaan tehdyissä analyyseissa pääosin kumariinipitoisuuksilla. Näissä analyyseissa uutenäytteet pääosin ryhmittyivät ja erottuivat kokonaiskumariinipitoisuuksien mukaan. Myös aaltolukualueen 1785-725 cm-1 analyyseissa havaittiin keräyspaikan mukaan ryhmittymistä, mitä kumariinipitoisuudet eivät kuitenkaan selittäneet. Näihin ryhmittymisiin vaikuttivat mahdollisesti muiden yhdisteiden samanlaiset pitoisuudet näytteissä. Analyyseissa käytettiin myös muita aaltolukualueita, mutta tulokset eivät juuri poikenneet aiemmista. 2. kertaluvun derivaattaspektrien monimuuttuja-analyysit sormenjälkialueelta eivät myöskään muuttaneet tuloksia havaittavasti. Jatkotutkimuksissa nyt käytettyä menetelmää on mahdollista edelleen kehittää esimerkiksi tutkimalla monimuuttuja-analyyseissa 2. kertaluvun derivaattaspektreistä suppeampia, tarkkaan valittuja aaltolukualueita.

CHITRA: Cognitive handprinted input-trained recursively analyzing system for recognition of alphanumeric characters

A novel system for recognition of handprinted alphanumeric characters has been developed and tested. The system can be employed for recognition of either the alphabet or the numeral by contextually switching on to the corresponding branch of the recognition algorithm. The two major components of the system are the multistage feature extractor and the decision logic tree-type catagorizer. The importance of ldquogoodrdquo features over sophistication in the classification procedures was recognized, and the feature extractor is designed to extract features based on a variety of topological, morphological and similar properties. An information feedback path is provided between the decision logic and the feature extractor units to facilitate an interleaved or recursive mode of operation. This ensures that only those features essential to the recognition of a particular sample are extracted each time. Test implementation has demonstrated the reliability of the system in recognizing a variety of handprinted alphanumeric characters with close to 100% accuracy.

Purification and properties of trehalase from the thermophilic fungus Humicola lanuginosa

Trehalase (?,?-Trehalosee gludohydrolase, EC 3.2.1.28) was partially solubilized from the thermophilic fungus Humicola lanuginosa RM-B, and purified 184-fold. The purified enzyme was optimally active at 50°C in acetate buffer at pH 5.5. It was highly specific for ?,?-trehalose and had an apparent Km = 0.4 mM at 50°C. None of the other disaccharides tested either inhibited or activated the enzyme. The molecular weight of the enzyme was around 170000. Trehalase from mycelium grown at 40 and 50°C had similar properties. The purified enzyme, in contrast to that in the crude-cell free extract, was less stable. At low concentration, purified trehalase was afforded protection against heat-inactivation by �protective factor(s)� present in mycelial extracts. The �protective factor(s)� was sensitive to proteolytic digestion. It was not diffusable and was stable to boiling for at least 30 min. Bovine serum albumin and casein also protected the enzyme from heat-inactivation.

Vision based indoor positioning in a retail environment

Modern smart phones often come with a significant amount of computational power and an integrated digital camera making them an ideal platform for intelligents assistants. This work is restricted to retail environments, where users could be provided with for example navigational in- structions to desired products or information about special offers within their close proximity. This kind of applications usually require information about the user's current location in the domain environment, which in our case corresponds to a retail store. We propose a vision based positioning approach that recognizes products the user's mobile phone's camera is currently pointing at. The products are related to locations within the store, which enables us to locate the user by pointing the mobile phone's camera to a group of products. The first step of our method is to extract meaningful features from digital images. We use the Scale- Invariant Feature Transform SIFT algorithm, which extracts features that are highly distinctive in the sense that they can be correctly matched against a large database of features from many images. We collect a comprehensive set of images from all meaningful locations within our domain and extract the SIFT features from each of these images. As the SIFT features are of high dimensionality and thus comparing individual features is infeasible, we apply the Bags of Keypoints method which creates a generic representation, visual category, from all features extracted from images taken from a specific location. A category for an unseen image can be deduced by extracting the corresponding SIFT features and by choosing the category that best fits the extracted features. We have applied the proposed method within a Finnish supermarket. We consider grocery shelves as categories which is a sufficient level of accuracy to help users navigate or to provide useful information about nearby products. We achieve a 40% accuracy which is quite low for commercial applications while significantly outperforming the random guess baseline. Our results suggest that the accuracy of the classification could be increased with a deeper analysis on the domain and by combining existing positioning methods with ours.