Boundary Extraction in Images Using Hierarchical Clustering-based Segmentation

Hierarchical organization is one of the main characteristics of human segmentation. A human subject segments a natural image by identifying physical objects and marking their boundaries up to a certain level of detail [1]. Hierarchical clustering based segmentation (HCS) process mimics this capability of the human vision. The HCS process automatically generates a hierarchy of segmented images. The hierarchy represents the continuous merging of similar, spatially adjacent or disjoint, regions as the allowable threshold value of dissimilarity between regions, for merging, is gradually increased. HCS process is unsupervised and is completely data driven. This ensures that the segmentation process can be applied to any image, without any prior information about the image data and without any need for prior training of the segmentation process with the relevant image data. The implementation details of HCS process have been described elsewhere in the author's work [2]. The purpose of the current study is to demonstrate the performance of the HCS process in outlining boundaries in images and its possible application in processing medical images. [1] P. Arbelaez. Boundary Extraction in Natural Images Using Ultrametric Contour Maps. Proceedings 5th IEEE Workshop on Perceptual Organization in Computer Vision (POCV'06). June 2006. New York, USA. [2] A. N. Selvan. Highlighting Dissimilarity in Medical Images Using Hierarchical Clustering Based Segmentation (HCS). M. Phil. dissertation, Faculty of Arts Computing Engineering and Sciences Sheffield Hallam Univ., Sheffield, UK, 2007.</p

Hierarchical Clustering-based Segmentation (HCS) Aided Interpretation of Multi-parametric MR Images of the Prostate

BACKGROUND: Tissue abnormality is usually related to a dissimilar part of an otherwise homogeneous image. Choosing the optimal post processing threshold value can be difficult because the image composition may vary depending on the acquisition parameters and the type of tissue. Hierarchical Clustering-based Segmentation (HCS) is an approach to Computer Aided Monitoring (CAM) that enables a user to define a region of interest and the process generates a hierarchy of segmentation results to highlight the varied dissimilarities that might be present. HCS allows the user to derive the maximum benefit from the computational capability (perception) of the machine while at the same time, enabling them to incorporate their own interpretation in the appropriate place. This achieves a complementary synthesis of both computer and human strengths [1]. Aim of the Study: HCS PROCESS AS AN AID TO DIAGNOSIS IN mpMRI OF PROSTATE To evaluate HCS process as semi-quantitative analytical tool, to complement radiologist's interpretation of mpMR images of prostate METHOD: In prostate cancer, the leaky characteristics of the tumour angiogenesis, is demonstrated in DCE-MRI by the early rapid high enhancement just after the administration of contrast medium followed immediately by a relatively rapid decline. In comparison there will be a lower and continuously increasing enhancement for normal tissues. The above characteristics can be demonstrated by the quantitative measurement of signal enhancement in DCE-MRI with time i.e. Time Intensity Curve (TIC). The characteristic shape of the TIC (Figure 2) may be used for supporting diagnosis. Within the user defined ROI, the HCS process is applied to the DCE-MRI temporal frame of a slice of interest identified by the user. For qualitative analysis, for dissimilar regions, HCS process provides following (Fig. 3A, B) heat map, regions coloured as per their TIC types and correlation with T2 regions. For quantitative analysis, parametric image of the time intensity curves of the contrast wash-in, wash-out process are plotted for suspicious regions confirmed by user (Fig. 3C)

Computer aided monitoring of breast abnormalities in X-ray mammograms

X­ray mammography is regarded as the most effective tool for the detection and diagnosis of breast cancer, but the interpretation of mammograms is a difficult and error­prone task. Computer­aided detection (CADe) systems address the problem that radiologists often miss signs of cancers that are retrospectively visible in mammograms. Furthermore, computer­aided diagnosis (CADx) systems assist the radiologist in the classification of mammographic lesions as benign or malignant[1]. This paper details a novel alternative system namely computer­aided monitoring (CAM) system. The designed CAM system can be used to objectively measure the properties of a suspected abnormal area in a mammogram. Thus it can be used to assist the clinician to objectively monitor the abnormality. For instance its response to treatment and consequently its prognosis. The designed CAM system is implemented using the Hierarchical Clustering based Segmentation (HCS) [2] [3] [4] process. Brief description of the implementation of this CAM system is as follows : Using the approximate location and size of the abnormality, obtained from the user, the HCS process automatically identifies the more appropriate boundaries of the different regions within a region of interest (ROI), centred at the approximate location. From the set of, HCS process segmented, regions the user identifies the regions which most likely represent the abnormality and the healthy areas. Subsequently the CAM system compares the characteristics of the user identified abnormal region with that of the healthy region; to differentiate malignant from benign abnormality. In processing sixteen mammograms from mini­MIAS [5], the designed CAM system demonstrated a success rate of 100% in differentiating malignant from benign abnormalities

Sustainable Development Goals: Tracing Social Media’s Tamil Citizen Voices in Indian context

As per Human Development Index of 2019 report, India is in the 129th rank. Nearly 28% of the Indian population lives below the poverty line. As per another report from The Lancet journal on healthcare index, India is in 154th position among the 195 countries. According to India’s Annual Status of Education Report 2017, 14% of children in the age group of 14-18 are not enrolled in any school system. Education, health and living standards are the primary factors to measure the quality of life in any social settings. Giving this grim view of social conditions in India, the role of public institutions as well as individual members’ are crucial in lending adequate support to the society to enhance the standings of living conditions. According to a study published in 2011, only 2% of news space were being given to the issues pertinent to development by the leading Indian national newspapers. Many news stories in these 2%, might have appeared due to 'Coups and earthquakes syndrome’ type of events that contained news values of death, disease and disaster. From the perspective of Sustainable Development Goals, the participatory approach of individuals is more meaningful in order to achieve the stated 17 goals as well as 169 targets. Apart from the government’s initiative for the SDGs and substantial fundings from the donor agencies, awareness about the SDGs is essential and ideal for the bottom-up approach towards achieving the development goals and its targets by 2030. In this respect, with the help of twitteR package of and text mining tools of R programming, citizen’s voice was measured for those keywords and its equivalent hashtags which were appeared in June 2018. English is being the elite language in India, one regional language Tamil would be included to compare the non-English private sphere with regard to SDGs in Indian context

Dynamic Contrast Enhanced (DCE) MRI : Hierarchical Clustering-based Segmentation (HCS) as an aid to diagnosis

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has become an important component in the diagnostic imaging pathway and is emerging as a useful clinical technique for evaluating the severity, location, and extent of primary and recurrent cancer. But DCE-MRI typically generates around 30 images per section and image interpretation requires substantial experience to detect and categorize lesions. Tissue abnormality is usually related to a dissimilar part of an otherwise homogeneous image. Choosing the optimal post processing threshold value can be difficult because the image composition may vary depending on the acquisition parameters and the type of tissue. Hierarchical Clustering-based Segmentation (HCS) is an approach to Computer Aided Monitoring (CAM) that enables a user to define a region of interest and the process generates a hierarchy of segmentation results to highlight the varied dissimilarities that might be present. This new HCS process based CAM system offers a versatile and flexible environment by allowing the user to derive the maximum benefit from the computational capability (perception) of the machine. At the same time, the user is able to incorporate their own interpretation in the appropriate place and thus limit the machine's interpretive function to achieve a complementary synthesis of both computer and human strengths. As a diagnostic aid for the analysis of DCE-MRI image data, the process starts with the user defining a rough region of interest (ROI) on a section/slice of choice. Within the user defined ROI, the HCS process is applied to all the DCE-MRI temporal frames of the slice. HCS process output provides heat map images based on the normalised average pixel value of the various dissimilar regions within the ROI. Time intensity curves of the contrast wash-in, wash-out process are then plotted for suspicious regions confirmed by the user. Early results suggest that the HCS process based CAM system offers increased capability to differentiate suspicious areas by combining users' expertise and computer system's processing capability. The application is useful at the point of first diagnosis because often lesions are not solitary in nature, which can result in an incomplete treatment regime and affect prognosis; and also in monitoring the effects of drugs or radiotherapy. Ongoing research applications of the HCS process based CAM system are prostate, breast, knee, brain, and liver imaging. An example of the HCS process based CAM system for prostate is outlined below.</p

DCE MRI : hierarchical clustering-based segmentation as an aid to diagnosis.

Early results suggest that the HCS process based Computer Aided Monitoring (CAM) system offers increased capability to differentiate suspicious areas by combining users' expertise and computer system's processing capability. The application, using border pixel reclassification to highlight key areas, is particularly useful at the point of first diagnosis because lesions are commonly not solitary in nature, which can result in an incomplete treatment regime and affect prognosis; and also in monitoring the effects of drugs or radiotherapy

Hierarchical clustering-based segmentation (HCS) aided interpretation of the DCE MR Images of the Prostate

In Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) for prostate cancer, there is early intense enhancement and rapid washout of contrast material, due to the heterogeneous and leaky characteristics of the tumour angiogenesis. These characteristics can be demonstrated by the quantitative measurement of signal enhancement with time (Time Intensity Curve). The TIC is plotted for the pixels', averaged intensity value, within a user drawn Region of Interest (ROI). The ROI, normally chosen within an area of the largest enhancement, may enclose tissues of different enhancement pattern. Hence the averaged TIC from the ROI may not represent the actual characteristics of the enclosed tissue of interest. Hierarchical Clustering-based Segmentation (HCS) is an approach to Computer Aided Monitoring (CAM) that generates a hierarchy of segmentation results to highlight the varied dissimilarities in images. As a diagnostic aid for the analysis of DCE-MR image data, the process starts with the HCS process applied to all the DCE-MR temporal frames of a slice. HCS process output provides heat map images based on the normalised average pixel value of the various dissimilar regions. TIC of the contrast wash-in, wash-out process are then plotted for suspicious regions confirmed by the user. In this paper we have demonstrated how the HCS process as asemi-quantitative analytical tool to analyse the DCE MR images of the Prostate complements the radiologist's interpretation of DCE MR images

Thermo-mechanical properties of sodium chloride and alkali-treated sugarcane bagasse fibre

The experimental characterization of mechanical and thermal properties of treated and raw sugarcane bagasse fibre hasbeen studied. The bagasse fibres are treated with sodium chloride (NaCl) and sodium hydroxide (NaOH) solutions. TheNaOH treated fibres show better structural and thermal properties than other two types. SEM image of alkali-treated fibresreveals that the bundles of fibres are mainly composed of thin parenchyma cell walls. The fibres are joined with each otherwhich improves the mechanical properties. The statistical analysis is also performed using ANOVA one-factor method.From ANOVA, the significant difference between the dependent parameters and the various chemical treatments aredetermined. The results show that the NaCl and NaOH treated fibres significantly improve the mechanical properties andthermal stability

Hierarchical Cluster Analysis to Aid Diagnostic Image Data Visualization of MS and Other Medical Imaging Modalities

Perceiving abnormal regions in the images of different medical modalities plays a crucial role in diagnosis and subsequent treatment planning. In medical images to visually perceive abnormalities’ extent and boundaries requires substantial experience. Consequently, manually drawn region of interest (ROI) to outline boundaries of abnormalities suffers from limitations of human perception leading to inter-observer variability. As an alternative to human drawn ROI, it is proposed the use of a computer-based segmenta- tion algorithm to segment digital medical image data. Hierarchical Clustering-based Segmentation (HCS) process is a generic unsupervised segmentation process that can be used to segment dissimilar regions in digital images. HCS process generates a hierarchy of segmented images by partitioning an image into its constituent regions at hierarchical levels of allowable dissimilarity between its different regions. The hierarchy represents the continuous merging of similar, spatially adjacent, and/or disjoint regions as the allowable threshold value of dissimilarity between regions, for merging, is gradually increased. This chapter discusses in detail first the implementation of the HCS process, second the implementa- tion details of how the HCS process is used for the presentation of multi-modal imaging data (MALDI and MRI) of a biological sample, third the implementation details of how the process is used as a perception aid for X-ray mammogram readers, and finally the implementation details of how it is used as an interpreta- tion aid for the interpretation of Multi-parametric Magnetic Resonance Imaging (mpMRI) of the Prostate

Hierarchical clustering-based segmentation (HCS) aided diagstic image interpretation monitoring.

Machines are good at operations which require precision and computing objective measures. In contrast, humans are good at generalisation and making decisions based on their past experience and heuristics. Hence, to solve any problem with a solution involving human-machine interaction, it is imperative that the tasks are shared appropriately. However, the boundary which divides these two different set of tasks is not well defined in domains such as medical image interpretation. Therefore, one needs a versatile tool which is flexible enough to accommodate the varied requirements of the user. The aim of this study is to design and implement such a software tool to aid the radiologists in the interpretation of diagnostic images.Tissue abnormality in a medical image is usually related to a dissimilar part of an otherwise homogeneous image. The dissimilarity may be subtle or strong depending on the medical modality and the type of abnormal tissue. Hierarchical Clustering-based Segmentation (HCS) process is a dissimilarity highlighting process that yields a hierarchy of segmentation results. In this study, the HCS process was investigated for offering the user a versatile and flexible environment to perceive the varied dissimilarities that might be present in diagnostic images. Consequently, the user derives the maximum benefit from the computational capability (perception) of the machine and at the same time incorporate their own decision process (interpretation) at the appropriate places.As a result of the above investigation, this study demonstrates how HCS process can be used to aid radiologists in their interpretive tasks. Specifically this study has designed the following HCS process aided diagnostic image interpretation applications: interpretation of computed tomography (CT) images of the lungs to quantitatively measure the dimensions of the airways and the accompanying blood vessels; Interpretation of X-ray mammograms to quantitatively differentiate benign from malignant abnormalities. One of the major contribution of this study is to demonstrate how the above HCS process aided interpretation of diagnostic images can be used to monitor disease conditions. This thesis details the development and evaluation of the novel computer aided monitoring (CAM) system. The designed CAM system is used to objectively measure the properties of suspected abnormal areas in the CT images of the lungs and in X-ray mammogram. Thus, the CAM system can be used to assist the clinician to objectively monitor the abnormality. For instance, its response to treatment and consequently its prognosis. The implemented CAM system to monitor abnormalities in X-ray mammograms is briefly described below. Using the approximate location and size of the abnormality, obtained from the user, the HCS process automatically identifies the more appropriate boundaries of the different regions within a region of interest (ROI), centred at the approximate location. From the set of, HCS process segmented, regions the user identifies the regions which most likely represent the abnormality and the healthy areas. Subsequently, the CAM system compares the characteristics of the user identified abnormal region with that of the healthy region; to differentiate malignant from benign abnormality. In processing sixteen mammograms, the designed CAM system demonstrated the possibility of successfully differentiating malignant from benign abnormalities