Color and morphological features extraction and nuclei classification in tissue samples of colorectal cancer

Cancer is an important public health problem and the third most leading cause of death in North America. Among the highest impact types of cancer are colorectal, breast, lung, and prostate. This thesis addresses the features extraction by using different artificial intelligence algorithms that provide distinct solutions for the purpose of Computer-AidedDiagnosis (CAD). For example, classification algorithms are employed in identifying histological structures, such as lymphocytes, cancer-cells nuclei and glands, from features like existence, extension or shape. The morphological aspect of these structures indicates the degree of severity of the related disease. In this paper, we use a large dataset of 5000 images to classify eight different tissue types in the case of colorectal cancer. We compare results with another dataset. We perform image segmentation and extract statistical information about the area, perimeter, circularity, eccentricity and solidity of the interest points in the image. Finally, we use and compare four popular machine learning techniques, i.e., Naive Bayes, Random Forest, Support Vector Machine and Multilayer Perceptron to classify and to improve the precision of category assignation. The performance of each algorithm was measured using 3 types of metrics: Precision, recall and F1-Score representing a huge contribution to the existing literature complementing it in a quantitative way. The large number of images has helped us to circumvent the overfitting and reproducibility problems. The main contribution is the use of new characteristics different from those already studied, this work researches about the color and morphological characteristics in the images that may be useful for performing tissue classification in colorectal cancer histology

Maastikumeetrika ja ökosüsteemi kultuuriteenused – ressursipõhine integreeriv lähenemine maastikuharmoonia kaardistamisele

A Thesis for applying for the degree of Doctor of Philosophy in Environmental Protection.The overall idea of PhD thesis was to explain with objective evidence and using mapping techniques, why and how people value particular visual landscapes. Mainstream mapping research usually refers to uniqueness, diversity and naturalness of landscapes as the main factors for landscape values and preferences. These variables can be easily measured using satellite imagery and cartographic materials: for example, the diversity of landscape elements can be assessed with a function of Shannon information entropy, and naturalness – as the share of relatively natural land cover within the region of interest. However, psychological background suggests other important attributes of landscape experience – harmony, unity or coherence of the scene. Mentioned aspects are usually measured subjectively with questionnaires and surveys. Measuring landscape preferences is also quite a challenging task, requiring many people involved in assessment of photographs or even having a nature trip (with obvious drawbacks in spatial coverage and replicability with other evaluators). Therefore, the PhD research was designed to make all assessments as objective, as possible. Overall landscape coherence, for the first time, was measured as the extent to which total diversity of digital landscape model (composed of landforms and land cover) exceeds the added diversity of landforms and land cover alone. In this way, coherence was directly related to system properties of landscape, making it legible and understandable. Also, for the first time colour harmony of land cover was evaluated with remotely sensed data (satellite imagery). Retrieved map-based indices were examined with geo-located photographs of landscapes and outdoor recreation, uploaded to social media, such as Flickr, VK.com and former Panoramio. The study contributes to the operationalisation of landscape beauty and, therefore, more advanced landscape management, nature protection and sustainability of land use practises.Doktoritöö eesmärk on kaardistustehnoloogiad kasutades tõenduspõhiselt selgitada, miks ja kuidas inimesed väärtustavad teatud maastikke visuaalsest seisukohast. Peavoolu kaardistusuuringud tavaliselt keskenduvad maastiku väärtuste ja eelistuste hindamisel unikaalsusele, mitmekesisusele ja looduslikkusele. Neid muutujaid saab satelliitpiltide ja kartograafilise materjali põhjal lihtsalt mõõta, näiteks maastikuelementide mitmekesisust saab hinnata Shannoni entroopiavalemiga ning looduslikkust vastava iseloomuga maakatte osakaaluga uuritaval alal. Psühholoogilisest vaatepunktist lähtudes on maastikukogemusel veel teisi olulisi omadusi, nagu vaate harmoonia, ühtsus või kooskõla sidusus. Uuringute puhul mõõdetakse neid muutujaid tavaliselt subjektiivselt. Maastikueelistuste teaduslik hindamine on tõsine metoodiline väljakutse, mis nõuab paljude hindajate osalemist näiteks maastikufotode hindamisel või vahetult looduses, kus tuleb arvestada piirangutega ruumilisel esindatusel või hinnangute replikatiivsusel. Arvestades eelnimetatud asjaolusid, on dissertatsiooni eesmärgiks seatud leida võimalikult objektiivseid teid tavaliselt subjektiivsetena käsitletavate maastikumuutujate hindamisel. Uudne on üldise maastiku kooskõla mõõdetmine digitaalse pinnavorme ja maakatet hõlmava maastikumudeliga, võrreldes nende komponentide eraldi mõõtmisega. Selliselt menetledes on koherentsus otseselt seostatav maastiku struktuursete parameetritega ja seega muudab hinnangud loetavamaks ja arusaadavamaks. Esmakordselt on kaugseire andmete (satelliitpildid) alusel hinnatud ka maakatte värviharmooniat. Määratletud kaardipõhiseid indekseid kontrolliti kohtseotud fotodega maastikuvaadetest ning välirekreatsiooni tegevustest sotsiaalmeedias (nt Flickr, VK.com ja varasem Panoramio). Uuring aitab paremini mõista ja rakendada maastiku ilu hindamise käiku ja seeläbi kasutada esteetilist kvaliteeti maastiku planeerimisel ja korraldamisel, looduskaitses ja teistes säästva maakasutuse praktilistes valdkondades.Publication of this dissertation has been supported by the Estonian University of Life Science

A Pixon-based Image Segmentation Method Considering Textural Characteristics of Image

Image segmentation is an essential and critical process in image processing and pattern recognition. In this paper we proposed a textured-based method to segment an input image into regions. In our method an entropy-based textured map of image is extracted, followed by an histogram equalization step to discriminate different regions. Then with the aim of eliminating unnecessary details and achieving more robustness against unwanted noises, a low-pass filtering technique is successfully used to smooth the image. As the next step, the appropriate pixons are extracted and delivered to a fuzzy c-mean clustering stage to obtain the final image segments. The results of applying the proposed method on several different images indicate its better performance in image segmentation compared to the other methods

Image synthesis based on a model of human vision

Modern computer graphics systems are able to construct renderings of such high quality that viewers are deceived into regarding the images as coming from a photographic source. Large amounts of computing resources are expended in this rendering process, using complex mathematical models of lighting and shading. However, psychophysical experiments have revealed that viewers only regard certain informative regions within a presented image. Furthermore, it has been shown that these visually important regions contain low-level visual feature differences that attract the attention of the viewer. This thesis will present a new approach to image synthesis that exploits these experimental findings by modulating the spatial quality of image regions by their visual importance. Efficiency gains are therefore reaped, without sacrificing much of the perceived quality of the image. Two tasks must be undertaken to achieve this goal. Firstly, the design of an appropriate region-based model of visual importance, and secondly, the modification of progressive rendering techniques to effect an importance-based rendering approach. A rule-based fuzzy logic model is presented that computes, using spatial feature differences, the relative visual importance of regions in an image. This model improves upon previous work by incorporating threshold effects induced by global feature difference distributions and by using texture concentration measures. A modified approach to progressive ray-tracing is also presented. This new approach uses the visual importance model to guide the progressive refinement of an image. In addition, this concept of visual importance has been incorporated into supersampling, texture mapping and computer animation techniques. Experimental results are presented, illustrating the efficiency gains reaped from using this method of progressive rendering. This visual importance-based rendering approach is expected to have applications in the entertainment industry, where image fidelity may be sacrificed for efficiency purposes, as long as the overall visual impression of the scene is maintained. Different aspects of the approach should find many other applications in image compression, image retrieval, progressive data transmission and active robotic vision

A New Approach to Automatic Saliency Identification in Images Based on Irregularity of Regions

This research introduces an image retrieval system which is, in different ways, inspired by the human vision system. The main problems with existing machine vision systems and image understanding are studied and identified, in order to design a system that relies on human image understanding. The main improvement of the developed system is that it uses the human attention principles in the process of image contents identification. Human attention shall be represented by saliency extraction algorithms, which extract the salient regions or in other words, the regions of interest. This work presents a new approach for the saliency identification which relies on the irregularity of the region. Irregularity is clearly defined and measuring tools developed. These measures are derived from the formality and variation of the region with respect to the surrounding regions. Both local and global saliency have been studied and appropriate algorithms were developed based on the local and global irregularity defined in this work. The need for suitable automatic clustering techniques motivate us to study the available clustering techniques and to development of a technique that is suitable for salient points clustering. Based on the fact that humans usually look at the surrounding region of the gaze point, an agglomerative clustering technique is developed utilising the principles of blobs extraction and intersection. Automatic thresholding was needed in different stages of the system development. Therefore, a Fuzzy thresholding technique was developed. Evaluation methods of saliency region extraction have been studied and analysed; subsequently we have developed evaluation techniques based on the extracted regions (or points) and compared them with the ground truth data. The proposed algorithms were tested against standard datasets and compared with the existing state-of-the-art algorithms. Both quantitative and qualitative benchmarking are presented in this thesis and a detailed discussion for the results has been included. The benchmarking showed promising results in different algorithms. The developed algorithms have been utilised in designing an integrated saliency-based image retrieval system which uses the salient regions to give a description for the scene. The system auto-labels the objects in the image by identifying the salient objects and gives labels based on the knowledge database contents. In addition, the system identifies the unimportant part of the image (background) to give a full description for the scene

An Implemented Approach for Potentially Breast Cancer Detection Using Extracted Features and Artificial Neural Networks

Breast cancer (B-cancer) detection is still complex and challenging problem, and in that case, we propose and evaluate a four-step approach to segment and detect B-cancer disease. Studies show that relying on pure naked-eye observation of experts to detect such diseases can be prohibitively slow and inaccurate in some cases. Providing automatic, fast, and accurate image-processing-and artificial intelligence-based solutions for that task can be of great realistic significance. The presented approach itself scans the whole mammogram and performs filtering, segmentation, features extraction, and detection in a succession mode. The feasibility of the proposed approach was explored on 32 commonly virulent images, and the recognition rate achieved in the detection step is 100 %; further, the approach is able to give reliable results on distorted medical images, since the approach is subjected to a rectification step. Finally, this study is very effectual in decreasing mortality and increasing the quality of treatment of early onset of B-cancer

Evaluating Text-to-Image GANs Performance: A Comparative Analysis of Evaluation Metrics

Generative Adversarial Networks (GANs) have emerged as powerful techniques for generating high-quality images in various domains but assessing how realistic the generated images are is a challenging task. To address this issue, researchers have proposed a variety of evaluation metrics for GANs, each with its own strengths and limitations. This paper presents a comprehensive analysis of popular GAN evaluation metrics, including FID, Mode Score, Inception Score, MMD, PSNR, and SSIM. The strengths, weaknesses, and calculation processes of these metrics are discussed, focusing on assessing image fidelity and diversity. Two approaches, pixel distance, and feature distance, are employed to measure image similarity, while the importance of evaluating individual objects using input captions is emphasized. Experimental results on a basic GAN trained on the MNIST dataset demonstrate improvement in various metrics across different epochs. The FID score decreases from 497.54594 at Epoch 0 to 136.91156 at Epoch 100, indicating improved differentiation between real and generated images. In addition, the Inception Score increases from 1.1533 to 1.6408, reflecting enhanced image quality and diversity. These findings highlight the effectiveness of the GAN model in generating more realistic and diverse images with training progression.  However, when it comes to evaluating GANs on complex datasets, challenges arise, highlighting the need to combine evaluation metrics with visual inspection and subjective measures of image quality. By adopting a comprehensive evaluation approach, researchers can gain a deeper understanding of GAN performance and guide the development of advanced models

Design of a Real-Time Method for Detection and Evaluation of Corrosion in Vehicles

Automobiles endure several challenges when operating on the road that can degrade their performance, functionality, appearance, and overall utility. Although, corrosion is very ancient, it is the most dangerous hazard to an automobile. Corrosion can be defined as natural interaction between the metal and its surrounding atmosphere which results in oxidation of metal. This leads to change in metal properties and can be severely dangerous. One of the easiest ways to recognize corrosion is by using visual inspection methods. Visual inspection results are highly dependent on the operator’s way of analyzing corrosion and operator’s experience. Thus, visual inspection method lack standardization and is susceptible to human errors. In this research, an automated digital method is proposed to detect the surface corrosion and estimate the damage caused. The new approach has been designed to work effectively irrespective of the illumination levels, image dis-orientation and variance in rust texture. The proposed method in proven to be 96% accurate. Furthermore, the proposed method is designed in the form of a noncommercial, cloud-oriented app which is efficient, fast, low-cost, low-maintenance and possesses global accessibility

Grounding semantics in robots for Visual Question Answering

In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning

Exploring the relationships between perceived neighborhood boundaries and street network orientation

Tang, V., & Painho, M. (2023). Exploring the relationships between perceived neighborhood boundaries and street network orientation. Transactions in GIS, 27(3), 877-899. https://doi.org/10.1111/tgis.13058 --- Funding: The authors acknowledge the funding from the Portuguese national funding agency for science, research, and technology (Fundação para a Ciência e a Tecnologia—FCT) through the CityMe project (EXPL/GES-URB/1429/2021; https://cityme.novaims.unl.pt/).The neighborhood is a core unit of analysis in urban research, planning, and policy-making. However, perceptual and historical processes oftentimes result in neighborhoods that are not tied to sub-urban jurisdictions. For instance, historic neighborhoods might lack official spatial definitions, hampering neighborhood-based tasks in local offices. In this case, urban practitioners can benefit from readily available spatial proxies, such as the local street network. In this study, we conducted an exploratory analysis that combines neighborhood mapping and street network modeling. By retrieving participants' sketched boundaries and quantifying spatial orientations of sketched polygons and local network patterns, we were able to measure and compare the relationships between the urban fabric and the perceived extents of two historic neighborhoods in Lisbon, Portugal. The results provided insights for enhancing existing definitions of non-official neighborhoods, outlining new urban districts as well as for discussions about the role of the urban form in shaping people's perceptions.publishersversionpublishe