HII: Histogram Inverted Index For Fast Images Retrieval

This work aims to improve the speed of search by creating an indexing structure in CBIR system. We utilised an inverted index structure that usually used in text retrieval with a modification. The modified inverted index is built based on histogram data that generated using Multi Texton Histogram (MTH) and Multi Texton Co-Occurrence Descriptor (MTCD) from 10,000 images of Corel dataset. When building the inverted index, we normalised value of each feature into a real number and considered pairs of feature and value that owned by a particular number of images. Based on our investigation, on MTCD histogram of 5,000 data test, we found that by considering histogram variable values which owned by maximum 12% of images, the number of comparison for each query can be reduced by 67.47% in a rate, the precision is 82.2%, and the rate of access to disk is 32.83%. Furthermore, we named our approach as Histogram Inverted Index (HII).

A Comparative Analysis of the Zernike Moments for Single Object Retrieval

تم استخدام لحظات زينرك بشكل شائع في العديد من دراسات استرجاع الصور المبنية على الأشكال بسبب تمثيلها القوي للأشكال.ومع ذلك لم يتم تسليط الضوء بوضوح على نقاط قوتها وضعفها في الدراسات السابقة. وبالتالي، لا يمكن استغلال تمثيلها القوي بشكل كامل.في هذه االدراسة، يتم تنفيذ طريقة لالتقاط خصائص تمثيل الأشكال في لحظات زينرك بالكامل وتطبيقها على كائن واحد للحصول على صورثنائية ومستوى رمادي. تعمل الطريقة المقترحة عن طريق تحديد حدود كائن الشكل ثم تغيير حجم شكل الكائن إلى حدود الصورة. تم إجراءثلاث دراسات حالة. الحالة 1 هي تطبيق لحظات زينرك على صورة كائن الشكل الأصلي. في الحالة 2 ، يتم نقل النقطة الوسطى لصورةكائن الشكل في الحالة 1 إلى مركز الصورة. في الحالة 3 ، تقوم الطريقة المقترحة أولاً باكتشاف الحدود الخارجية لشكل الكائن ثم تغيير حجمالكائن إلى حد الصورة. تم إجراء تحقيقات تجريبية باستخدام مجموعتي بيانات صورتين قياسيتين أظهرت أن الطريقة المقترحة في الحالة 3قد تم توضيحها لتوفير أفضل أداء لاسترجاع الصور مقارنةً بكل من 1 و 2 . الخلاصة تتلخص ان لالتقاط الصورة بشكل كامل خصائص تمثيلالشكل القوية للحظة زينرك ، يجب تغيير حجم كائن الشكل إلى حدود الصورة .Zernike Moments has been popularly used in many shape-based image retrieval studies due to its powerful shape representation. However its strength and weaknesses have not been clearly highlighted in the previous studies. Thus, its powerful shape representation could not be fully utilized. In this paper, a method to fully capture the shape representation properties of Zernike Moments is implemented and tested on a single object for binary and grey level images. The proposed method works by determining the boundary of the shape object and then resizing the object shape to the boundary of the image. Three case studies were made. Case 1 is the Zernike Moments implementation on the original shape object image. In Case 2, the centroid of the shape object image in Case 1 is relocated to the center of the image. In Case 3, the proposed method first detect the outer boundary of the shape object and then resizing the object to the boundary of the image. Experimental investigations were made by using two benchmark shape image datasets showed that the proposed method in Case 3 had demonstrated to provide the most superior image retrieval performances as compared to both the Case 1 and Case 2. As a conlusion, to fully capture the powerful shape representation properties of the Zernike moment, a shape object should be resized to the boundary of the image

Deep Domain Adaptation Learning Framework for Associating Image Features to Tumour Gene Profile

While medical imaging and general pathology are routine in cancer diagnosis, genetic sequencing is not always assessable due to the strong phenotypic and genetic heterogeneity of human cancers. Image-genomics integrates medical imaging and genetics to provide a complementary approach to optimise cancer diagnosis by associating tumour imaging traits with clinical data and has demonstrated its potential in identifying imaging surrogates for tumour biomarkers. However, existing image-genomics research has focused on quantifying tumour visual traits according to human understanding, which may not be optimal across different cancer types. The challenge hence lies in the extraction of optimised imaging representations in an objective data-driven manner. Such an approach requires large volumes of annotated image data that are difficult to acquire. We propose a deep domain adaptation learning framework for associating image features to tumour genetic information, exploiting the ability of domain adaptation technique to learn relevant image features from close knowledge domains. Our proposed framework leverages the current state-of-the-art in image object recognition to provide image features to encode subtle variations of tumour phenotypic characteristics with domain adaptation techniques. The proposed framework was evaluated with current state-of-the-art in: (i) tumour histopathology image classification and; (ii) image-genomics associations. The proposed framework demonstrated improved accuracy of tumour classification, as well as providing additional data-derived representations of tumour phenotypic characteristics that exhibit strong image-genomics association. This thesis advances and indicates the potential of image-genomics research to reveal additional imaging surrogates to genetic biomarkers, which has the potential to facilitate cancer diagnosis

Biological inspired inspection underwater robot (SNAKEY)

This paper presents the designing and development of biological inspired inspection underwater robot. Inspection and monitoring activities have been applied in this project. Two medium involve in this project development. Land has been consider as a normal surface or medium with addition or been specialized in underwater region. Inspection activity is done using a camera at the front of the robot. The monitor display will be the user computer with addition of software and a converter to interface between camera and the computer. The ability to move can be controlled by the user. There are 7 servos been used with 8 segments been design including the head of the robot. The mechanism that been apply is side winding movement and the angle for servo is ±30 degree. The speed of the robot is 0.072 kmh-1 in land and 0.18 kmh-1 on water. This robot can capture and record using the software that been used to make the inspection activity runs perfectly

Radiogenomics Framework for Associating Medical Image Features with Tumour Genetic Characteristics

Significant progress has been made in the understanding of human cancers at the molecular genetics level and it is providing new insights into their underlying pathophysiology. This progress has enabled the subclassification of the disease and the development of targeted therapies that address specific biological pathways. However, obtaining genetic information remains invasive and costly. Medical imaging is a non-invasive technique that captures important visual characteristics (i.e. image features) of abnormalities and plays an important role in routine clinical practice. Advancements in computerised medical image analysis have enabled quantitative approaches to extract image features that can reflect tumour genetic characteristics, leading to the emergence of ‘radiogenomics’. Radiogenomics investigates the relationships between medical imaging features and tumour molecular characteristics, and enables the derivation of imaging surrogates (radiogenomics features) to genetic biomarkers that can provide alternative approaches to non-invasive and accurate cancer diagnosis. This thesis presents a new framework that combines several novel methods for radiogenomics analysis that associates medical image features with tumour genetic characteristics, with the main objectives being: i) a comprehensive characterisation of tumour image features that reflect underlying genetic information; ii) a method that identifies radiogenomics features encoding common pathophysiological information across different diseases, overcoming the dependence on large annotated datasets; and iii) a method that quantifies radiogenomics features from multi-modal imaging data and accounts for unique information encoded in tumour heterogeneity sub-regions. The present radiogenomics methods advance radiogenomics analysis and contribute to improving research in computerised medical image analysis

Strategies for image visualisation and browsing

PhDThe exploration of large information spaces has remained a challenging task even though the proliferation of database management systems and the state-of-the art retrieval algorithms is becoming pervasive. Signi cant research attention in the multimedia domain is focused on nding automatic algorithms for organising digital image collections into meaningful structures and providing high-semantic image indices. On the other hand, utilisation of graphical and interactive methods from information visualisation domain, provide promising direction for creating e cient user-oriented systems for image management. Methods such as exploratory browsing and query, as well as intuitive visual overviews of image collection, can assist the users in nding patterns and developing the understanding of structures and content in complex image data-sets. The focus of the thesis is combining the features of automatic data processing algorithms with information visualisation. The rst part of this thesis focuses on the layout method for displaying the collection of images indexed by low-level visual descriptors. The proposed solution generates graphical overview of the data-set as a combination of similarity based visualisation and random layout approach. Second part of the thesis deals with problem of visualisation and exploration for hierarchical organisation of images. Due to the absence of the semantic information, images are considered the only source of high-level information. The content preview and display of hierarchical structure are combined in order to support image retrieval. In addition to this, novel exploration and navigation methods are proposed to enable the user to nd the way through database structure and retrieve the content. On the other hand, semantic information is available in cases where automatic or semi-automatic image classi ers are employed. The automatic annotation of image items provides what is referred to as higher-level information. This type of information is a cornerstone of multi-concept visualisation framework which is developed as a third part of this thesis. This solution enables dynamic generation of user-queries by combining semantic concepts, supported by content overview and information ltering. Comparative analysis and user tests, performed for the evaluation of the proposed solutions, focus on the ways information visualisation a ects the image content exploration and retrieval; how e cient and comfortable are the users when using di erent interaction methods and the ways users seek for information through di erent types of database organisation