The series "Advances in Intelligent Systems and Computing" contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia.    The publications within "Advances in Intelligent Systems and Computing" are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results.   Indexed by DBLP, EI Compendex, INSPEC, WTI Frankfurt eG, zbMATH, Japanese Science and Technology Agency (JST), SCImago.  All books published in the series are submitted for consideration in Web of Science.

Advances in Intelligent Systems and Computing

| All drivers have habits behind the wheel. Different drivers vary in how they hit the gas and brake pedals, how they turn the steering wheel, and how much following distance they keep to follow a vehicle safely and comfortably. In this paper, we model such driving behaviors as car-following and pedal operation patterns. The relationship between following distance and velocity mapped into a two-dimensional space is modeled for each driver with an optimal velocity model approximated by a nonlinear function or with a statistical method of a Gaussian mixture model (GMM). Pedal operation patterns are also modeled with GMMs that represent the distributions of raw pedal operation signals or spectral features extracted through spectral analysis of the raw pedal operation signals. The driver models are evaluated in driver identification experiments using driving signals collected in a driving simulator and in a real vehicle. Experimental results show that the driver model based on the spectral features of pedal operation signals efficiently models driver individual differences and achieves an identification rate of 76.8% for a field test with 276 drivers, resulting in a relative error reduction of 55% over driver models that use raw pedal operation signals without spectral analysis.

Driver modeling based on driving behavior and its evaluation in driver identification : Analysis of car-pedal use and car-following habits may allow the help offered by intelligent driver assistance systems to be customized for individual drivers

Medical image registration is a highlight of actual research on medical image processing. It play an important role in clinical diagnoses and treating.Mutul information has been deeply researched and popularly applied in image registration. We discuss the principle, virtue, p- rogress and improvement of mutual information(MI). At last, other two similarity measures, a normalized similarity measure based on Jensen′s difference and f-information, are analyzede.

A survey of medical image registration

Implementation of machine vision for detecting behaviour of cattle and pigs

https://repositorio.inesctec.pt/bitstream/123456789/8308/1/P-00P-X1V.pdf

CATARACTS: Challenge on automatic tool annotation for cataRACT surgery

Granular computing in model based abdominal organs detection

Purpose 
A growing number of medical applications, including minimal invasive surgery, depends on multi-modal or multi-sensors data processing. Fast and accurate 3D scene analysis, comprising data registration, seems to be crucial for the development of computer aided diagnosis and therapy. The advancement of surface tracking system based on optical trackers already plays an important role in surgical procedures planning. However, new modalities, like the time-of-flight (ToF) sensors, widely explored in non-medical fields are powerful and have the potential to become a part of computer aided surgery set-up. Connection of different acquisition systems promises to provide a valuable support for operating room procedures. Therefore, the detailed analysis of the accuracy of such multi-sensors positioning systems is needed.


Methods 
We present the system combining pre-operative CT series with intra-operative ToF-sensor and optical tracker point clouds. The methodology contains: optical sensor set-up and the ToF-camera calibration procedures, data pre-processing algorithms, and registration technique. The data pre-processing yields a surface, in case of CT, and point clouds for ToF-sensor and marker-driven optical tracker representation of an object of interest. An applied registration technique is based on Iterative Closest Point algorithm.


Results 
The experiments validate the registration of each pair of modalities/sensors involving phantoms of four various human organs in terms of Hausdorff distance and mean absolute distance metrics. The best surface alignment was obtained for CT and optical tracker combination, whereas the worst for experiments involving ToF-camera.


Conclusion 
The obtained accuracies encourage to further develop the multi-sensors systems. The presented substantive discussion concerning the system limitations and possible improvements mainly related to the depth information produced by the ToF-sensor is useful for computer aided surgery developers.

/pdf/time-of-flight-camera-optical-tracker-and-computed-1uhsf531n6.pdf

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration.

The paper presents the development of a radiological atlas employed in an abdomen patient specific model verification.

Radiological Atlas for Patient Specific Model Generation

The fast development of imaging techniques during last decades makes it possible to introduce intra-operative visualization as the integral part of surgical procedures. Therefore, the automated analysis of intra-operative ultrasound images is appreciated. The image processing, registration and visualization techniques help in better understanding and locate the operated region. To meet these needs, the paper presents an advanced algorithm for automated detection of tissue deformations caused by a biopsy needle. For this, feature set of Histogram of Gradients (HoG) is introduced. The extracted feature vectors are then used in image cell clustering step resulting in tissue deformation as well as biopsy needle detection. The applied there Kernelized Weighted C-Means clustering technique enables robust and accurate needle detection proven by sensitivity and specificity values at levels of 0.846 and 0.99, respectively.

HoG feature based detection of tissue deformations in ultrasound data

This study presents the development of an image navigation system dedicated to assist a miniinvasive abdominal surgery. First, the system overview is discussed. Then, a development of a mesh model of abdominal structure is presented. Next, a system calibration including the component as well as patient registration is described.The evaluation of the system has been performed separately for each component. The accuracy of calibration as well as patient registration is reported. This yields the robustnes evaluation of the overall system.

Bartłomiej Pyciński

Papers

Granular computing in model based abdominal organs detection

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration.

Radiological Atlas for Patient Specific Model Generation

HoG feature based detection of tissue deformations in ultrasound data

Image Navigation in Minimally Invasive Surgery