Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Quantization Error Minimization by Reducing Median Difference at Quantization Interval Class

In this paper, a new technique to define the size of quantization interval is defined. In general, high quantization error will occur if large interval is used at a large difference value class whereas low quantization error will occur if a small interval is used at a large difference value class. However, the existence of too many class intervals will lead to a higher system complexity. Thus, this research is mainly about designing a quantization algorithm that can provide an efficient interval as possible to reduce the quantization error. The novelty of the proposed algorithm is to utilize the high occurrence of zero coefficient by re-allocating the non-zero coefficient in a group for quantization. From the experimental results provided, this new algorithm is able to produce a high compressed image without compromising with the image quality

The new fuzzy analytical hierarchy process with interval type-2 trapezoidal fuzzy sets and its application

The degree of type-1 fuzzy sets membership function cannot express the linguistic variable of a complex problem. The type-2 fuzzy sets as a problem solver such that more fuzziness for constructing membership functions can be handled. Recently, many multi-criteria decision making (MCDM) methods have been expanded using type-2 fuzzy sets. Analytical Hierarchy Process (AHP) is one of the well-known MCDM that can take into account multiple and conflicting criteria at the same time. Our goal is to develop an interval type-2 trapezoidal fuzzy AHP through the new proposed ranking i.e. the modified total integral value. Based on the illustrative examples for trapezoidal type-2 fuzzy sets, the new proposed ranking has a well-performance in ranking. Furthermore, we apply the new trapezoidal type-2 fuzzy AHP to a supplier selection problem. Based on the results of the application, the new fuzzy AHP has the same ranking results as the existing fuzzy AHP

Consolidating Literature for Images Compression and Its Techniques

With the proliferation of readily available image content, image compression has become a topic of considerable importance. As, rapidly increase of digital imaging demand, storage capability aspect should be considered. Therefore, image compression refers to reducing the size of image for minimizing storage without harming the image quality. Thus, an appropriate technique is needed for image compression for saving capacity as well as not losing valuable information. This paper consolidates literature whose characteristics have focused on image compression, thresholding algorithms, quantization algorithms. Later, related research on these areas are presented

Compression of image sequences in interactive medical teleconsultations

Interactive medical teleconsultations are an important tool in the modern medical practice. Their applications include remote diagnostics, conferences, workshops and classes for students. In many cases standard medium or low-end machines are employed and the teleconsultation systems must be able to provide high quality of user experience with very limited resources. Particularly problematic are large datasets, consisting of image sequences, which need to be accessed fluently. The main issue is insufficient internal memory, therefore proper compression methods are crucial. However, a scenario where image sequences are kept in a compressed format in the internal memory and decompressed on-the-fly when displayed, is difficult to implement due to performance issues. In this paper we present methods for both lossy and lossless compression of medical image sequences, which require only compatibility with Pixel Shader 2.0 standard, which is present even on relatively old, low-end devices. Based on the evaluation of quality, size reduction and performance, the methods are proved to be suitable and beneficial for the medical teleconsultation applications

The Effect on Compressed Image Quality using Standard Deviation-Based Thresholding Algorithm

In recent decades, digital images have become increasingly important. With many modern applications use image graphics extensively, it tends to burden both the storage and transmission process. Despite the technological advances in storage and transmission, the demands placed on storage and bandwidth capacities still exceeded its availability. Compression is one of the solutions to this problem but elimination some of the data degrades the image quality. Therefore, the Standard Deviation-Based Thresholding Algorithm is proposed to estimate an accurate threshold value for a better-compressed image quality. The threshold value is obtained by examining the wavelet coefficients dispersion on each wavelet subband using Standard Deviation concept. The resulting compressed image shows a better image quality with PSNR value above 40dB

Thyroid Segmentation and Volume Estimation Using CT Images

ABSTRACT: Pathology of thyroid gland is determined by physicians with its volume as a significant indicator.For this thyroid area segmentation and volume estimation are necessary steps. Most physicians use CT images even if the volume of thyroid gland is determined using Ultrasound images, for precise evaluation of volume of thyroid gland. In this paper a Linear Vector Quantization neural network (LVQNN) with a pre-processing procedure and initial segmentation using cellular automata(CA) is proposed for thyroid segmentation and volume estimation using computerized tomography (CT) images

طراحی روشی کارآمد در فشرده سازی چند مرحله‌ای تصاویر ماموگرافی جهت ذخیره سازی و انتقال بهینه بر مبنای شبکه‌های عصبی مصنوعی و الگوریتم L-M

سابقه و هدف: در فرایند پزشکی از راه دور (Telemedicine)، استفاده از تکنیک‌های دیجیتالی در تشخیص بیماری‌ها سبب شده تا پزشکان جهت آرشیو و نگهداری اطلاعات بیماران به منابع ذخیره‌سازی و نیز پهنای باند بالا در انتقال داده‌ها نیاز پیدا کنند. مواد و روش‌ها: هدف از ارائه این مقاله، معرفی یک شیوه کارآمد در فشرده‌سازی چند مرحله‌ای اطلاعات مربوط به تصاویر ماموگرافی بر اساس شبکه‌های عصبی مصنوعی و الگوریتم L-M است. در ابتدا تصویر ماموگرافی با ورود به شبکه عصبی، این امکان را خواهد داشت که با کمترین میزان تخریب و درجه فشردگی بالا در لایه نخست فشرده شود. یافته‌ها: پیاده سازی مراحل فشرده‌سازی تصاویر ماموگرافی با استفاده از تصاویر 128 زن با سنین 55/6±41/46 سال و شاخص توده بدنی 5/5±78/36 از سطح 3 کلینیک‌ تخصصی از شهر سبزوار صورت گرفت و مشاهده شد که به ترتیب متوسط مجذورات خطا برابر (MSE) 24/4، بیشترین نسبت تفاوت برابر 46/33 و نسبت فشرده سازی 8:1 در خروجی الگوریتم حاصل آمدند؛ عملکرد قابل قبول سیستم بر اساس طراحی دقیق نرم افزاری بوده و به همین دلیل کارایی مناسبی را در عمل به همراه دارد. نتیجه‌گیری: بر مبنای قابلیت اطمینان به خروجی نرم افزار در فشرده‌سازی و انتشار و به دلیل عدم تخریب اطلاعات اساسی تصاویر ماموگرافی در زمان فشرده شدن، تشخیص در مرحله اکتشاف با تشخیص در واقعیت مطابقت بالایی دارد و از این رو سیستم امکان پیاده‌سازی را در مراکز بیمارستانی در آرشیو تصاویر ماموگرافی داراست و انجام آن را توجیه می‌کند