Nurulhuda Firdaus Mohd Azmi

Quantitative measure in image segmentation for skin lesion images: A preliminary study

Automatic Skin Lesion Diagnosis (ASLD) allows skin lesion diagnosis by using a computer or mobile devices. The idea of using a computer to assist in diagnosis of skin lesions was first proposed in the literature around 1985. Images of skin lesions are analyzed by the computer to capture certain features thought to be characteristic of skin diseases. These features (expressed as numeric values) are then used to classify the image and report a diagnosis. Image segmentation is often a critical step in image analysis and it may use statistical classification, thresholding, edge detection, region detection, or any combination of these techniques. Nevertheless, image segmentation of skin lesion images is yet limited to superficial evaluations which merely display images of the segmentation results and appeal to the readers intuition for evaluation. There is a consistent lack of quantitative measure, thus, it is difficult to know which segmentation present useful results and in which situations they do so. If segmentation is done well, then, all other stages in image analysis are made simpler. If significant features (that are crucial for diagnosis) are not extracted from images, it will affect the accuracy of the automated diagnosis. This paper explore the existing quantitative measure in image segmentation ranging in the application of pattern recognition for example hand writing, plat number, and colour. Selecting the most suitable segmentation measure is highly important so that as much relevant features can be identified and extracted.

Data Quality Issues in Big Data: A Review

Data with good quality has precedence when analyzing and using big data to deduce value from such tremendous volume of data in today’s business environments. Decisions and insights derived from poor data has a negative and unpredictable consequences to organizations. At present, due to the lack of comprehensive and intensive research in the field of data quality, especially large data, there is an urgent need to address this issue by researchers to reach the optimal way to estimate and evaluate the quality of large data. Thus, enabling institutions to make rational decisions based on evaluation outputs. In this paper, the current research on the quality of large data was reviewed and summarized by exploring the basic characteristics of large data. The main challenges facing the quality of information were also discussed in the context of large data. Some of the initiatives suggested by the researchers to evaluate the quality of the data have been highlighted. Finally, we believe that the results of these reviews will enhance the conceptual measurements of the large data quality and produce a concrete groundwork for the future by creating an integrated data quality assessment and evaluation models using the suitable algorithms.

The challenges of Extract, Transform and Loading (ETL) system implementation for near real-time environment

Organization with considerable investment into data warehousing, the influx of various data types and forms requires certain ways of prepping data and staging platform that support fast, efficient and volatile data to reach its targeted audiences or users of different business needs. Extract, Transform and Load (ETL) system proved to be a choice standard for managing and sustaining the movement and transactional process of the valued big data assets. However, traditional ETL system can no longer accommodate and effectively handle streaming or near real-time data and stimulating environment which demands high availability, low latency and horizontal scalability features for functionality. This paper identifies the challenges of implementing ETL system for streaming or near real-time data which needs to evolve and streamline itself with the different requirements. Current efforts and solution approaches to address the challenges are presented. The classification of ETL system challenges are prepared based on near real-time environment features and ETL stages to encourage different perspectives for future research.

ABCD rules segmentation on malignant tumor and benign skin lesion images

Skin lesion is defined as a superficial growth or patch of the skin that is visually different than its surrounding area. Skin lesions appear for many reasons such as the symptoms indicative of diseases, birthmarks, allergic reactions, and so on. Images of skin lesions are analyzed by computer to capture certain features to be characteristic of skin diseases. These activities can be defined as automated skin lesion diagnosis (ASLD). ASLD involves five steps including image acquisition, pre-processing to remove occluding artifacts (such as hair), segmentation to extract regions of interest, feature selection and classification. This paper present analysis of automated segmentation called the ABCD rules (Asymmetry, Border irregularity, Color variegation, Diameter) in image segmentation. The experiment was carried on Malignant tumor and Benign skin lesion images. The study shows that the ABCD rules has successfully classify the images with high value of total dermatoscopy score (TDS). Although some of the analysis shows false alarm result, it may give the significant input to search suitable segmentation measure.