Automatic classification of colorectal and prostatic histologic tumor images using multiscale multispectral local binary pattern texture features and stacked generalization

This paper proposes a new multispectral multiscale local binary pattern feature extraction method for automatic classification of colorectal and prostatic tumor biopsies samples. A multilevel stacked generalization classification technique is also proposed and the key idea of the paper considers a grade diagnostic problem rather than a simple malignant versus tumorous tissue problem using the concept of multispectral imagery in both the visible and near infrared spectra. To validate the proposed algorithm performances, a comparative study against related works using multispectral imagery is conducted including an evaluation on three different multiclass datasets of multispectral histology images: two representing images of colorectal biopsies - one dataset was acquired in the visible spectrum while the second captures near-infrared spectra. The proposed algorithm achieves an accuracy of 99.6% on the different datasets. The results obtained demonstrate the advantages of infrared wavelengths to capture more efficiently the most discriminative information. The results obtained show that our proposed algorithm outperforms other similar methods

Remote sensing technologies for the assessment of marine and coastal ecosystems

Abstract This chapter reviews the Remote Sensing (RS) technologies that are particularly appropriate for marine and coastal ecosystem research and management. RS techniques are used to perform analysis of water quality in coastal water bodies; to identify, characterize and analyze river plumes; to extract estuarine/coastal sandy bodies; to identify beach features/patterns; and to evaluate the changes and integrity (health) of the coastal lagoon habitats. For effective management of these ecosystems, it is essential to have satellite data available and complementary accurate information about the current state of the coastal regions, in addition to well-informed forecasts about its future state. In recent years, the use of space, air and ground-based RS strategies has allowed for the rapid data collection, Image processing (Pixel-Based and Object-Based Image Analysis (OBIA) classification) and dissemination of such information to reduce vulnerability to natural hazards, anthropic pressures, and to monitoring essential ecological processes, life support systems and biological diversityinfo:eu-repo/semantics/submittedVersio

Geodiversiteetin tutkiminen kaukokartoituksella

Tiivistelmä. Tutkielman tarkoitus on selvittää, miten kaukokartoitusmenetelmiä voidaan hyödyntää geodiversiteetin tutkimisessa. Geodiversiteetin merkitys elollisen luonnon perustana on vasta viime vuosikymmeninä alettu ymmärtämään ja siihen kohdistuvan tutkimuksen määrä kasvaa. Kaukokartoitusmenetelmät kehittyvät jatkuvasti, ja tietokoneiden kapasiteetin kasvaessa kaukokartoitusaineistojen käsittely tehostuu, avaten uusia mahdollisuuksia myös geodiversiteettitutkimuksessa. Tietoa geodiversiteetistä tarvitaan, jotta osataan arvioida ihmistoiminnan vaikutuksia, varautua ilmastonmuutokseen ja suunnata ympäristöön kohdistuvia suojelutoimia mahdollisimman hyvin. Kaukokartoitusmenetelmistä lähemmässä tarkastelussa tässä tutkielmassa ovat Lidar-menetelmä sekä multi- ja hyperspektraalisten sensoreiden käyttö. Tutkielmassa käydään myös läpi, miten aineiston pohjalta voidaan tutkia tietyn alueen geodiversiteetin määrä. Kyseisillä menetelmillä voidaan kartoittaa geodiversiteetin osia eri mittakaavoissa. Menetelmiä yhdistelemällä voidaan saada sekä laaja että tarkka kuva tutkittavan alueen geodiversiteetin elementtien esiintymisestä. Eri aineistojen prosessointi, tulkitseminen ja yhdistäminen vaatii kuitenkin asiantuntijuutta. Lisäksi tarvittaisiin yhteneväisyyttä geodiversiteetin kvantifioimisen menetelmiin. Kaukokartoitusmenetelmät tarjoavat kustannustehokkaan ja laajojen alueiden tutkimiseen soveltuvan tavan kerätä aineistoa geodiversiteetistä. Aiemmin kerättyjä aineistoja voidaan myös hyödyntää ja saada siten ymmärrystä geodiversiteetissä tapahtuvista muutoksista. Tulevaisuudessa pilvipalvelujen ja yhä tehokkaampien tietokoneiden avulla myös kaukokartoitusmenetelmien käyttö geodiversiteetin tutkimisessa tulee helpottumaan ja monipuolistumaan entisestään

Automated classification of cancer tissues using multispectral imagery

Automated classification of medical images for colorectal and prostate cancer diagnosis is a crucial tool for improving routine diagnosis decisions. Therefore, in the last few decades, there has been an increasing interest in refining and adapting machine learning algorithms to classify microscopic images of tumour biopsies. Recently, multispectral imagery has received a significant interest from the research community due to the fast-growing development of high-performance computers. This thesis investigates novel algorithms for automatic classification of colorectal and prostate cancer using multispectral imagery in order to propose a system outperforming the state-of-the-art techniques in the field. To achieve this objective, several feature extraction methods based on image texture have been investigated, analysed and evaluated. A novel texture feature for multispectral images is also constructed as an adaptation of the local binary pattern texture feature to multispectral images by expanding the pixels neighbourhood to the spectral dimension. It has the advantage of capturing the multispectral information with a limited feature vector size. This feature has demonstrated improved classification results when compared against traditional texture features. In order to further enhance the systems performance, advanced classification schemes such as bag-of-features - to better capture local information - and stacked generalisation - to select the most discriminative texture features - are explored and evaluated. Finally, the recent years have seen an accelerated and exponential rise of deep learning, boosted by the advances in hardware, and more specifically graphics processing units. Such models have demonstrated excellent results for supervised learning in multiple applications. This observation has motivated the employment in this thesis of deep neural network architectures, namely convolutional neural networks. Experiments were also carried out to evaluate and compare the performance obtained with the features extracted using convolutional neural networks with random initialisation against features extracted with pre-trained models on ImageNet dataset. The analysis of the classication accuracy achieved with deep learning models reveals that the latter outperforms the previously proposed texture extraction methods. In this thesis, the algorithms are assessed using two separate multiclass datasets: the first one consists of prostate tumour multispectral images, and the second contains multispectral images of colorectal tumours. The colorectal dataset was acquired on a wide domain of the light spectrum ranging from the visible to the infrared wavelengths. This dataset was used to demonstrate the improved results produced using infrared light as well as visible light

Salinity hazard mapping and risk assessment in the Bourke irrigation district

At no point in history have we demanded so much from our agricultural land whilst simultaneously leaving so little room for management error. Of the many possible environmental impacts from agriculture, soil and water salinisation has some of the most long-lived and deleterious effects. Despite its importance, however, land managers are often unable to make informed decisions of how to manage the risk of salinisation due to a lack of data. Furthermore, there remains no universally agreed method for salinity risk mapping. This thesis addresses these issues by investigating new methods for producing high-resolution predictions of soil salinity, soil physical properties and groundwater depth using a variety of traditional and emerging ancillary data sources. The results show that the methodologies produce accurate predictions yielding natural resource information at a scale and resolution not previously possible. Further to this, a new methodology using fuzzy logic is developed that exploits this information to produce high-resolution salinity risk maps designed to aid both agricultural and natural resource management decisions. The methodology developed represents a new and effective way of presenting salinity risk and has numerous advantages over conventional risk models. The incorporation of fuzzy logic provides a meaningful continuum of salinity risk and allows for the incorporation of uncertainty. The method also allows salinity risk to be calculated relative to any vegetation community and shows where the risk is coming from (root-zone or groundwater) allowing more appropriate management decisions to be made. The development of this methodology takes us a step closer to closing what some have called our greatest gap in agricultural knowledge. That is, our ability to manage the salinity risk at the subcatchment scale

Remote sensing methods for biodiversity monitoring with emphasis on vegetation height estimation and habitat classification

Biodiversity is a principal factor for ecosystem stability and functioning, and the need for its protection has been identified as imperative globally. Remote sensing can contribute to timely and accurate monitoring of various elements related to biodiversity, but knowledge gap with user communities hinders its widespread operational use. This study advances biodiversity monitoring through earth observation data by initially identifying, reviewing, and proposing state-of-the-art remote sensing methods which can be used for the extraction of a number of widely adopted indicators of global biodiversity assessment. Then, a cost and resource effective approach is proposed for vegetation height estimation, using satellite imagery from very high resolution passive sensors. A number of texture features are extracted, based on local variance, entropy, and local binary patterns, and processed through several data processing, dimensionality reduction, and classification techniques. The approach manages to discriminate six vegetation height categories, useful for ecological studies, with accuracies over 90%. Thus, it offers an effective approach for landscape analysis, and habitat and land use monitoring, extending previous approaches as far as the range of height and vegetation species, synergies of multi-date imagery, data processing, and resource economy are regarded. Finally, two approaches are introduced to advance the state of the art in habitat classification using remote sensing data and pre-existing land cover information. The first proposes a methodology to express land cover information as numerical features and a supervised classification framework, automating the previous labour- and time-consuming rule-based approach used as reference. The second advances the state of the art incorporating Dempster–Shafer evidential theory and fuzzy sets, and proves successful in handling uncertainties from missing data or vague rules and offering wide user defined parameterization potential. Both approaches outperform the reference study in classification accuracy, proving promising for biodiversity monitoring, ecosystem preservation, and sustainability management tasks.Open Acces