Vianey Guadalupe Cruz Sánchez

Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine

BackgroundLung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool.MethodsThe stages of the proposed CADx are: a supervised extraction of the region of interest to eliminate the shape differences among CT images. The Daubechies db1, db2, and db4 wavelet transforms are computed with one and two levels of decomposition. After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules.ResultsThe clinical data set used for experiments consists of 45 CT scans from ELCAP and LIDC. For the training stage 61 CT images were used (36 with cancerous lung nodules and 25 without lung nodules). The system performance was tested with 45 CT scans (23 CT scans with lung nodules and 22 without nodules), different from that used for training. The results obtained show that the methodology successfully classifies cancerous nodules with a diameter from 2 mm to 30 mm. The total preciseness obtained was 82%; the sensitivity was 90.90%, whereas the specificity was 73.91%.ConclusionsThe CADx system presented is competitive with other literature systems in terms of sensitivity. The system reduces the complexity of classification by not performing the typical segmentation stage of most CADx systems. Additionally, the novelty of the algorithm is the use of a wavelet feature descriptor.

Smart Multi-Level Tool for Remote Patient Monitoring Based on a Wireless Sensor Network and Mobile Augmented Reality

Technological innovations in the field of disease prevention and maintenance of patient health have enabled the evolution of fields such as monitoring systems. One of the main advances is the development of real-time monitors that use intelligent and wireless communication technology. In this paper, a system is presented for the remote monitoring of the body temperature and heart rate of a patient by means of a wireless sensor network (WSN) and mobile augmented reality (MAR). The combination of a WSN and MAR provides a novel alternative to remotely measure body temperature and heart rate in real time during patient care. The system is composed of (1) hardware such as Arduino microcontrollers (in the patient nodes), personal computers (for the nurse server), smartphones (for the mobile nurse monitor and the virtual patient file) and sensors (to measure body temperature and heart rate), (2) a network layer using WiFly technology, and (3) software such as LabView, Android SDK, and DroidAR. The results obtained from tests show that the system can perform effectively within a range of 20 m and requires ten minutes to stabilize the temperature sensor to detect hyperthermia, hypothermia or normal body temperature conditions. Additionally, the heart rate sensor can detect conditions of tachycardia and bradycardia.

Lung nodule classification in frequency domain using support vector machines

In this paper a computational alternative to classify lung nodules inside CT thorax images in the frequency domain is presented. After image acquisition, a region of interest is manually selected. Then, the spectrums of the two dimensional Discrete Cosine Transform (2D-DCT) and the two dimensional Fast Fourier Transform (2D-FFT) were calculated. Later, two statistical texture features were extracted from the histogram computed from the spectrum of each CT image. Finally, a support vector machine with a radial basis function as a kernel was used as the classifier. Seventy five tests with different diagnosis and number of images were used to validate the methodology presented. After experimentation and results, ten false negatives (FN) and two false positives (FP) were obtained, and a sensitivity and specificity of 96.15% and 52.17% respectively. The total preciseness obtained with the methodology proposed was 82.66%.

A Comparison of the Bandelet, Wavelet and Contourlet Transforms for Image Denoising

The bandelet transform take advantage of the geometrical regularity of the structure of an image and is appropriate for the analysis of edges and textures of the images. Denoising is one of the most interesting and widely investigated topics in image processing area. The main problem in denosing is the tradeoff between the noise suppression and oversmoothing of image details. In order to solve that problem, in this paper we exploit the geometrical advantages offered by the bandelet transform to solve the problem of image denoising. We present the results obtained with the bandelet transform for denoising process with additive white Gaussian noise and salt and pepper noise. A comparison is made with those results obtained with wavelets and contourlets. We show that bandelets can outperform the wavelets and contourlets in terms of subjective and objective measures.

A mobile augmented reality system to support machinery operations in scholar environments

This paper proposes a mobile augmented reality (MAR) system aimed to support students in the use of a milling and lathe machines at a university manufacturing laboratory. The system incorporates 3D models of machinery and tools, text instructions, animations and videos with real processes to enrich the information obtained from the real world. The elements are shown when the user points the camera of a mobile device to specific parts of the machinery, where augmented reality (AR) markers are placed. The main goals of the project were (1) create an AR system that guides inexperienced users in machinery handling and (2) measure the acceptance rate and performance of the system in the school manufacturing laboratory. The guidance is provided by means of virtual information about how to operate the machinery when the trainer is not present. The system was implemented as a mobile app for Android devices and it was tested by 16 students and teachers at the university manufacturing laboratory through a survey. The results of this study revealed that students, laboratory technicians, and teachers had positive opinions and good acceptance about the use of the MAR system in the manufacturing laboratory.

Lung Nodule Classification in CT Thorax Images Using Support Vector Machines

In this paper a computational alternative to classify lung nodules using computed tomography (CT) thorax images is presented. The novelty of the method is the elimination of the segmentation stage. The contribution consist of several steps. After image acquisition, eight texture features were extracted from the histogram and the gray level coocurrence matrix (with four different angles) for each CT image. The features were used to train a non-parametric classifier called support vector machine (SVM), used to classify lung tissues into two classes: with lung nodules and without lung nodules. A total of 128 public clinical data set (ELCAP, NBIA) with different number of slices and diagnoses were used to train and evaluate the performance of the methodology presented. After the tests stage, five false negative (FN) and seven false positive (FP) results were obtained. The results obtained were validated by a radiologist to finally obtain a reliability index of 84%.

A Pilot Study on the Use of Mobile Augmented Reality for Interactive Experimentation in Quadratic Equations

Recent studies have reported that the inclusion of new technological elements such as augmented reality (AR), for educational purposes, increases the learning interest and motivation of students. However, developing AR applications, especially with mobile content, is still a rather technical subject; thus the dissemination of the technology in the classroom has been rather limited. This paper presents a new software architecture for AR application development based on freely available components; it provides a detailed view of the subsystems and tasks that encompass the creation of a mobile AR application. The typical task of plotting a quadratic equation was selected as a case study to obtain feasibility insights on how AR could support the teaching-learning process and to observe the student’s reaction to the technology and the particular application. The pilot study was conducted with 59 students at a Mexican undergraduate school. A questionnaire was created in order to obtain information about the students’ experience using the AR application and the analysis of the results obtained is presented. The comments expressed by the users after the AR experience are positive, supporting the premise that AR can be, in the future, a valuable complimentary teaching tool for topics that benefit from contextual learning experience and multipoint visualization, such as the quadratic equation.

The Nonsubsampled Contourlet Transform for Enhancement of Microcalcifications in Digital Mammograms

Microcalcifications detection plays a crucial role in the early detection of breast cancer. The enhancement of the mammographic images is one of the most important tasks during the detection process. This paper presents an algorithm for the enhancement of microcalcifications in digital mammograms. The main novelty is the application of the nonsubsampled contourlet transform and a specific edge filter to enhance the directional structures of the image in the contourlet domain. The inverse contourlet transform is applied to recover an approximation of the mammogram with the microcalcifications enhanced. Results show that the proposed method outperforms the current method based on the discrete wavelet transform.

Feature Preserving Image Compression: A Survey

With the increase of the use of Internet and wireless mobile devices, the digital information needs to be send and received efficiently in low bit rates in order to exploit the bandwidth. At low bit rates it is almost impossible to generate errors or artifacts in images. In order to solve that problem, researchers are trying to design and build lossy image coders which can preserve important features of images. With this approach the features of an image that are very important to perception and recognition are preserved even at low bit rates. In this paper a revision of some works that propose feature preserving image compression (FPIC) algorithms are presented. Finally a new methodology to obtain FPIC is presented

The H.264 Video Coding Standard

Since 1990, the Video Coding Expert Group (VCEG) and the Moving Pictures Expert Group (MPEG) have focused their research in video coding techniques for different applications. The International Telecommunication Union (ITU-T) and the MPEG groups joined in a single group to form the Joint Video Team (JVT) and worked together on a new video standard called H.264, H.264/AVC Advanced Video Coding, or MPEG-4 part 10, which achieves more coding efficiency with simpler syntax specifications than the previous standards, more integration with all network protocols, and multiplex architectures. The H.264 is the state-ofthe-art codec and covers a wide range of applications with excellent results such as videoconferencing, video streaming, and video transmission over fixed and wireless networks with different transport protocols among others.