Computer Aided Diagnostic Support System for Skin cancer: Review of techniques and algorithms

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique’s performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided

Computer aided diagnostic support system for skin cancer: A review of techniques and algorithms

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided. © 2013 Ammara Masood and Adel Ali Al-Jumaily

A Review on Skin Disease Classification and Detection Using Deep Learning Techniques

Skin cancer ranks among the most dangerous cancers. Skin cancers are commonly referred to as Melanoma. Melanoma is brought on by genetic faults or mutations on the skin, which are caused by Unrepaired Deoxyribonucleic Acid (DNA) in skin cells. It is essential to detect skin cancer in its infancy phase since it is more curable in its initial phases. Skin cancer typically progresses to other regions of the body. Owing to the disease's increased frequency, high mortality rate, and prohibitively high cost of medical treatments, early diagnosis of skin cancer signs is crucial. Due to the fact that how hazardous these disorders are, scholars have developed a number of early-detection techniques for melanoma. Lesion characteristics such as symmetry, colour, size, shape, and others are often utilised to detect skin cancer and distinguish benign skin cancer from melanoma. An in-depth investigation of deep learning techniques for melanoma's early detection is provided in this study. This study discusses the traditional feature extraction-based machine learning approaches for the segmentation and classification of skin lesions. Comparison-oriented research has been conducted to demonstrate the significance of various deep learning-based segmentation and classification approaches

Computer aided diagnosis system using dermatoscopical image

Computer Aided Diagnosis (CAD) systems for melanoma detection aim to mirror the expert dermatologist decision when watching a dermoscopic or clinical image. Computer Vision techniques, which can be based on expert knowledge or not, are used to characterize the lesion image. This information is delivered to a machine learning algorithm, which gives a diagnosis suggestion as an output. This research is included into this field, and addresses the objective of implementing a complete CAD system using ‘state of the art’ descriptors and dermoscopy images as input. Some of them are based on expert knowledge and others are typical in a wide variety of problems. Images are initially transformed into oRGB, a perceptual color space, looking for both enhancing the information that images provide and giving human perception to machine algorithms. Feature selection is also performed to find features that really contribute to discriminate between benign and malignant pigmented skin lesions (PSL). The problem of robust model fitting versus statistically significant system evaluation is critical when working with small datasets, which is indeed the case. This topic is not generally considered in works related to PSLs. Consequently, a method that optimizes the compromise between these two goals is proposed, giving non-overfitted models and statistically significant measures of performance. In this manner, different systems can be compared in a fairer way. A database which enjoys wide international acceptance among dermatologists is used for the experiments.Ingeniería de Sistemas Audiovisuale

3次元光音響顕微鏡における血管画像の再構築とフィルタリング手法

Tohoku University西條芳文課

Neural Correlates of Binaural Interaction Using Aggregate-System Stimulation in Cochlear Implantees

The importance of binaural cues in auditory stream formation and sound source differentiation is widely accepted. When treating one ear with a cochlear implant (CI) the peripheral auditory system gets partially replaced and processing delays get added potentially, thus important interaural time encoding gets altered. This is a crucial problem because factors like the interaural time delay between the receiving ears are known to be responsible for facilitating such cues, e.g., sound source localization and separation. However, these effects are not fully understood, leaving a lack of systematic binaural fitting strategies with respect to an optimal binaural fusion. To gain new insights into such alterations, we suggest a novel method of free-field evoked auditory brainstem response (ABR) analysis in CI users. As a result, this method does not bypass the technically induced intrinsic delays of the hearing device while leaving the complete electrode array active, thus the most natural way of stimulation is provided and the comparable testing of real world stimuli gets facilitated. Unfortunately, ABRs acquired in CI users are additionally affected by the prominent artifact caused by their electrical stimulation, which severely distorts the desired neural response, thus challenging their analysis. To circumvent this problem, we further introduce a novel narrowband filtering CI artifact removal technique capable of obtaining neural correlates of ABRs in CI users. Consequently, we were able to compare brainstem-level responses collected of 12 CI users and 12 normal hearing listeners using two different stimuli (i.e., chirp and click) at four different intensities each, what comprises an adaption of the prominent brainstem evoked response audiometry serving as an additional evaluation criterion. We analyzed the responses using the average of 2,000 trials in combination with synchronized regularizations across them and found consistent results in their deflections and latencies, as well as in single trial relationships between both groups. This method provides a novel and unique perspective into the natural CI users’ brainstem-level responses and can be practical in future research regarding binaural interaction and fusion. Furthermore, the binaural interaction component (BIC), i.e., the arithmetical difference between the sum of both monaurally evoked ABRs and the binaurally evoked ABR, has been previously shown to be an objective indicator for binaural interaction. This component is unfortunately known to be rather fragile and as a result, a reliable, objective measure of binaural interaction in CI users does not exist to the present date. It is most likely that implantees would benefit from a reliable analysis of brainstem-level and subsequent higher-level binaural interaction, since this could objectively support fitting strategies with respect to a maximization of interaural integration. Therefore, we introduce a novel method capable of obtaining neural correlates of binaural interaction in bimodal CI users by combining recent advances in the field of fast, deconvolution-based ABR acquisitions with the introduced narrowband filtering technique. The proposed method shows a significant improvement in the magnitude of resulting BICs in 10 bimodal CI users and a control-group of 10 normal hearing subjects when compensating the interaural latency difference caused by the technical devices. In total, both proposed studies objectively demonstrate technical-driven interaural latency mismatches. Thus, they strongly emphasize potential benefits when balancing these interaural delays to improve binaural processing by significant increases in associated neural correlates of successful binaural interaction. These results and also the estimated latency differences should be investigated in larger group sizes to further consolidate the results, but confirm the demand for rather binaural solutions than treating hearing losses in an isolated monaural manner.Zusammenfassung Die Notwendigkeit binauraler Verarbeitungsprozesse in der auditorischen Wahrnehmung ist weitestgehend akzeptiert. Bei der Therapie eines Ohres mit einem Cochlea-Implantat (engl. cochlear implant (CI)) wird das periphere auditorische System teilweise ersetzt und verändert, sodass natürliche, interaurale Zeitauflösungen beeinflusst werden. Dieses Problem ist entscheidend, denn Faktoren wie interaurale Laufzeitunterschiede zwischen den aufnehmenden Ohren sind verantwortlich für die Umsetzung der erwähnten binauralen Verarbeitungsprozesse, z.B. Schallquellenlokalisation und -separation. Allerdings sind diese Effekte nicht ausreichend verstanden, weshalb bis heute binaurale Anpassstrategien mit Rücksicht auf eine optimale Fusionierung fehlen. Um neue Einsichten in solche zeitlichen Verzerrungen zu erhalten, schlagen wir ein neues Verfahren der Freifeld evozierten auditorischen Hirnstammpotentiale (engl. auditory brainstem response (ABR)) in CI-Nutzern vor. Diese Methode beinhaltet explizit technisch-induzierte Laufzeiten verwendeter Hörhilfen, sodass eine natürliche Stimulation unter Verwendung von realitätsnahen Stimuli ermöglicht wird. Unglücklicherweise sind ABRs von CI-Nutzern zusätzlich mit Stimulationsartefakten belastet, wodurch benötigte neurale Antworten weiter verzerrt werden und eine entsprechende Analyse der Signale deutlich erschwert wird. Um dieses Problem zu umgehen, schlagen wir eine neue Artefakt- Reduktionstechnik vor, welche auf spektraler Schmalbandfilterung basiert und somit den Erhalt entsprechender, neuraler ABR Korrelate ermöglicht. Diese Methoden erlaubten die Interpretation neuraler Korrelate auf Hirnstammebene unter Verwendung von zwei verschiedenen Stimuli (Chirps und Klicks) unter vier verschiedenen Lautstärken in 12 CI-Nutzern und 12 normalhörenden Probanden. Die beschriebene Prozedur adaptiert somit die weitläufig bekannte Hirnstammaudiometrie (engl. brainstem evoked response audiometry (BERA)), deren Ergebnisse zur zusätzlichen Evaluation des vorgestellten Verfahrens dienten. Die Untersuchung der aus 2000 Einzelantworten erhaltenen Mittelwerte in Kombination mit der Analyse synchronisierter Regularitäten über den Verlauf der Einzelantworten ergab dabei konsistente Beobachtungen in gefundenen Amplituden, Latenzen sowie in Abhängigkeiten zwischen Einzelantworten in beiden Gruppen. Das vorgestellte Verfahren erlaubt somit auf einzigartige Weise neue und ungesehene Einsichten in natürliche, neurale Antworten auf Hirnstammebene von CI-Nutzern, welche in zukünftigen Studien verwendet werden können, um binaurale Interaktionen und Fusionen weiter untersuchen zu können. Interessanterweise hat sich, die auf ABRs basierende, binaurale Interaktionskomponente (engl. binaural interaction component (BIC)) als objektiver Indikator binauraler Integration etabliert. Diese Komponente (d.h. die arithmetische Differenz zwischen der Summe der monauralen Antworten und der binauralen Antwort) ist leider sehr fragil, wodurch ein sicherer und objektiver Nachweis in CI-Nutzern bis heute nicht existiert. Dabei ist es sehr wahrscheinlich, dass gerade Implantatsträger von einer entsprechenden Analyse auf Hirnstammebene und höherrangigen Ebenen deutlich profitieren würden, da dies objektiv Anpassstrategien mit Rücksicht auf eine bestmögliche binaurale Integration ermöglichen könnte. Deshalb stellen wir ein weiteres, neuartiges Verfahren zum Erhalt von neuralen Korrelaten binauraler Interaktion in bimodal versorgten CI-Trägern vor, welches jüngste Erfolge im Bereich der schnellen, entfalltungsbasierten ABR Akquisition und der bereits vorgestellten Schmalband- filterung zur Reduktion von Stimulationsartefakten kombiniert. Basierend auf diesem Verfahren konnten signifikante Verbesserungen in der BIC-Amplitude in 10 bimodal versorgten Patienten sowie 10 normalhörenden Probanden, basierend auf umgesetzte, interaurale Laufzeitkompensationen technischer Hörhilfen, aufgezeigt werden. Insgesamt demonstrieren beide vorgestellten Studien technisch-induzierte, interaurale Laufzeitunterschiede und betonen demnach sehr deutlich potenzielle Vorteile in assoziierten neuralen Korrelaten binauraler Interaktionen, wenn solche Missverhältnisse zeitlich ausgeglichen werden. Die aufgezeigten Ergebnisse sowie die getätigte Abschätzungen technischer Laufzeiten sollte in größeren Gruppen weiter untersucht werden, um die Aussagekraft weiter zu steigern. Dennoch unterstreichen diese Einsichten das Verlangen nach binauralen Lösungsansätzen in der zukünftigen Hörrehabilitation, statt bisheriger isolierter und monauraler Therapien

Changes in the EEG Spectrum of a Child with Severe Disabilities in Response to Power Mobility Training

Literature suggests that self-generated locomotion in infancy and early childhood enhances the development of various cognitive processes such as spatial awareness, social interaction, language development and differential attentiveness. Thus, having access to a power mobility device may play a crucial role for the overall development, mental health, and quality of life of children with multiple, severe disabilities who have limited motor control. This study investigates the feasibility of using electroencephalography (EEG) as an objective measure to detect changes in brain activity in a child due to power mobility training. EEG data was collected with a modified wireless neuroheadset using a single-subject A-B-A-B design consisting of two baseline phases (A) and two intervention phases (B). One trial consisted of three different activities during baseline phase; resting condition at the beginning (Resting 1) and at the end (Resting 2) of the trial, interaction with adults, and passive mobility. The intervention phase included a forth activity, the use of power mobility, while power mobility training was performed on another day within the same week of data collection. The EEG spectrum between 2.0 and 12.0 Hz was analyzed for Resting 1 and Resting 2 condition in each phase. We found significant increase of theta power and decrease in alpha power during all three phases following the first baseline. In respect of previous findings, these observations may be related to an increase in alertness and/or anticipation. Analysis of the percentage change from Resting 1 to Resting 2 condition revealed decrease in theta and increasing alpha power during the first intervention phase, which could be associated with increasing cognitive capacity immediately after the use of power mobility. Overall, no significant difference between baseline phase and intervention phase was observed. Thus, whether the observed changes may have been influenced or enhanced by power mobility training remains unclear and warrants further investigation

Multispectral iris recognition analysis: Techniques and evaluation

This thesis explores the benefits of using multispectral iris information acquired using a narrow-band multispectral imaging system. Commercial iris recognition systems typically sense the iridal reflection pertaining to the near-infrared (IR) range of the electromagnetic spectrum. While near-infrared imaging does give a very reasonable image of the iris texture, it only exploits a narrow band of spectral information. By incorporating other wavelength ranges (infrared, red, green, blue) in iris recognition systems, the reflectance and absorbance properties of the iris tissue can be exploited to enhance recognition performance. Furthermore, the impact of eye color on iris matching performance can be determined. In this work, a multispectral iris image acquisition system was assembled in order to procure data from human subjects. Multispectral images pertaining to 70 different eyes (35 subjects) were acquired using this setup. Three different iris localization algorithms were developed in order to isolate the iris information from the acquired images. While the first technique relied on the evidence presented by a single spectral channel (viz., near-infrared), the other two techniques exploited the information represented in multiple channels. Experimental results confirm the benefits of utilizing multiple channel information for iris segmentation. Next, an image enhancement technique using the CIE L*a*b* histogram equalization method was designed to improve the quality of the multispectral images. Further, a novel encoding method based on normalized pixel intensities was developed to represent the segmented iris images. The proposed encoding algorithm, when used in conjunction with the traditional texture-based scheme, was observed to result in very good matching performance. The work also explored the matching interoperability of iris images across multiple channels. This thesis clearly asserts the benefits of multispectral iris processing, and provides a foundation for further research in this topic

MEEGIPS—A modular EEG investigation and processing system for visual and automated detection of high frequency oscillations

High frequency oscillations (HFOs) are electroencephalographic correlates of brain activity detectable in a frequency range above 80 Hz. They co-occur with physiological processes such as saccades, movement execution, and memory formation, but are also related to pathological processes in patients with epilepsy. Localization of the seizure onset zone, and, more specifically, of the to-be resected area in patients with refractory epilepsy seems to be supported by the detection of HFOs. The visual identification of HFOs is very time consuming with approximately 8 h for 10 min and 20 channels. Therefore, automated detection of HFOs is highly warranted. So far, no software for visual marking or automated detection of HFOs meets the needs of everyday clinical practice and research. In the context of the currently available tools and for the purpose of related local HFO study activities we aimed at converging the advantages of clinical and experimental systems by designing and developing a comprehensive and extensible software framework for HFO analysis that, on the one hand, focuses on the requirements of clinical application and, on the other hand, facilitates the integration of experimental code and algorithms. The development project included the definition of use cases, specification of requirements, software design, implementation, and integration. The work comprised the engineering of component-specific requirements, component design, as well as component- and integration-tests. A functional and tested software package is the deliverable of this activity. The project MEEGIPS, a Modular EEG Investigation and Processing System for visual and automated detection of HFOs, introduces a highly user friendly software that includes five of the most prominent automated detection algorithms. Future evaluation of these, as well as implementation of further algorithms is facilitated by the modular software architecture.This work was supported by the Austrian Science Fund (FWF): KLI 657-B31 and by the PMU-FFF: A-18/01/029-HÖL.Peer reviewe