Efficient OCT Image Enhancement Based on Collaborative Shock Filtering

Efficient enhancement of noisy optical coherence tomography (OCT) images is a key task for interpreting them correctly. In this paper, to better enhance details and layered structures of a human retina image, we propose a collaborative shock filtering for OCT image denoising and enhancement. Noisy OCT image is first denoised by a collaborative filtering method with new similarity measure, and then the denoised image is sharpened by a shock-type filtering for edge and detail enhancement. For dim OCT images, in order to improve image contrast for the detection of tiny lesions, a gamma transformation is first used to enhance the images within proper gray levels. The proposed method integrating image smoothing and sharpening simultaneously obtains better visual results in experiments.

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Utilization potential of intraluminal optical coherence tomography for the Eustachian tube

Scientific Reports ◽

10.1038/s41598-021-85634-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hayoung Byun ◽

Yeon Hoon Kim ◽

Jingchao Xing ◽

Su-Jin Shin ◽

Seung Hwan Lee ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Eustachian Tube ◽

Balloon Dilatation ◽

Optical Coherence ◽

Cross Sectional ◽

Anatomical Structures ◽

Complex Anatomy ◽

Before And After ◽

Tubal Lumen ◽

Oct Image

AbstractImaging the Eustachian tube is challenging because of its complex anatomy and limited accessibility. This study fabricated a fiber-based optical coherence tomography (OCT) catheter and investigated its potential for assessing the Eustachian tube anatomy. A customized OCT system and an imaging catheter, termed the Eustachian OCT, were developed for visualizing the Eustachian tube. Three male swine cadaver heads were used to study OCT image acquisition and for subsequent histologic correlation. The imaging catheter was introduced through the nasopharyngeal opening and reached toward the middle ear. The OCT images were acquired from the superior to the nasopharyngeal opening before and after Eustachian tube balloon dilatation. The histological anatomy of the Eustachian tube was compared with corresponding OCT images, The new, Eustachian OCT catheter was successfully inserted in the tubal lumen without damage. Cross-sectional images of the tube were successfully obtained, and the margins of the anatomical structures including cartilage, mucosa lining, and fat could be successfully delineated. After balloon dilatation, the expansion of the cross-sectional area could be identified from the OCT images. Using the OCT technique to assess the Eustachian tube anatomy was shown to be feasible, and the fabricated OCT image catheter was determined to be suitable for Eustachian tube assessment.

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A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

Applied Sciences ◽

10.3390/app11125416 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5416

Author(s):

Yanheng Liu ◽

Minghao Yin ◽

Xu Zhou

Keyword(s):

Collaborative Filtering ◽

User Preference ◽

Computation Method ◽

Group Recommendation ◽

Preference Rating ◽

Filtering Method ◽

Personal Recommendation ◽

Collaborative Filtering Algorithm ◽

Group Members ◽

Individual Recommendation

The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.

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A Book Recommender System Using Collaborative Filtering Method

International Conference on Data Science, E-learning and Information Systems 2021 ◽

10.1145/3460620.3460744 ◽

2021 ◽

Author(s):

Sewar Khalifeh ◽

Amjed A. Al-Mousa

Keyword(s):

Collaborative Filtering ◽

Recommender System ◽

Filtering Method

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Variable lateral size imaging of the human retina in vivo by combined confocal/en face optical coherence tomography with closed loop OPD-locked low coherence interferometry based active axial eye motion tracking

10.1117/12.843070 ◽

2010 ◽

Cited By ~ 2

Author(s):

Radu G. Cucu ◽

Mark W. Hathaway ◽

Adrian G. Podoleanu ◽

Richard B. Rosen

Keyword(s):

Optical Coherence Tomography ◽

Motion Tracking ◽

Closed Loop ◽

Human Retina ◽

Lateral Size ◽

Low Coherence ◽

Low Coherence Interferometry ◽

En Face ◽

Eye Motion

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Song Recommendations Based on Artists with Cosine Similarity Algorithms and K-Nearest Neighbor

JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) ◽

10.24843/jlk.2020.v08.i04.p01 ◽

2020 ◽

Vol 8 (4) ◽

pp. 367

Author(s):

Muhammad Arief Budiman ◽

Gst. Ayu Vida Mastrika Giri

Keyword(s):

Collaborative Filtering ◽

Mobile Phones ◽

Recommendation System ◽

Nearest Neighbor ◽

Cosine Similarity ◽

Music Recommendation ◽

K Nearest Neighbor ◽

Filtering Method ◽

K Nearest Neighbor Algorithm ◽

Music Recommendation System

The development of the music industry is currently growing rapidly, millions of music works continue to be issued by various music artists. As for the technologies also follows these developments, examples are mobile phones applications that have music subscription services, namely Spotify, Joox, GrooveShark, and others. Application-based services are increasingly in demand by users for streaming music, free or paid. In this paper, a music recommendation system is proposed, which the system itself can recommend songs based on the similarity of the artist that the user likes or has heard. This research uses Collaborative Filtering method with Cosine Similarity and K-Nearest Neighbor algorithm. From this research, a system that can recommend songs based on artists who are related to one another is generated.

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