A Convolutional Neural Network (CNN) based Pill Image Retrieval System

Several works have been done in the area of image retrieval systems, and many are still trying to provide improvements for a better model for retrieving said images. Image segmentation using clustering techniques is one of the most used approaches. There are various clustering methods available, but the non-linear k-means clustering technique is the most used method. In the following research, a model of retrieving images using a non-linear classifier aided with a convolutional neural network is proposed. Both algorithms were exploited and paired in terms of feature extraction and classification. Comprehensive evaluations over a dataset containing over 7,000 pill images of 1,000 pill types obtained from the National Library of Medicine database demonstrate significant success during the data classification using the proposed model

Nuclei segmentation of histology images based on deep learning and color quantization and analysis of real world pill images

Medical image analysis has paved a way for research in the field of medical and biological image analysis through the applications of image processing. This study has special emphasis on nuclei segmentation from digitized histology images and pill segmentation. Cervical cancer is one of the most common malignant cancers affecting women. This can be cured if detected early. Histology image feature analysis is required to classify the squamous epithelium into Normal, CIN1, CIN2 and CIN3 grades of cervical intraepithelial neoplasia (CIN). The nuclei in the epithelium region provide the majority of information regarding the severity of the cancer. Segmentation of nuclei is therefore crucial. This paper provides two methods for nuclei segmentation. The first approach is clustering approach by quantization of the color content in the histology images uses k-means++ clustering. The second approach is deep-learning based nuclei segmentation method works by gathering localized information through the generation of superpixels and training convolutional neural network. The other part of the study covers segmentation of consumer-quality pill images. Misidentified and unidentified pills constitute a safety hazard for both patients and health professionals. An automatic pill identification technique is essential to address this challenge. This paper concentrates on segmenting the pill image, which is crucial step to identify a pill. A color image segmentation algorithm is proposed by generating superpixels using the Simple Linear Iterative Clustering (SLIC) algorithm and merging the superpixels by thresholding the region adjacency graphs. The algorithm manages to supersede the challenges due to various backgrounds and lighting conditions of consumer-quality pill images --Abstract, page iii

PillTank

Imagine an elderly family member, going through their daily routine of taking their pills. They find their pill box; however, they are having trouble identifying all the pills in there. Is there a name on the tablet? Can they read what it says? Do they just trust that the medication in their box is correct? How can they properly take care of themselves if they can not even confirm that what they are taking is the right medication? To combat this issue that many face, we present PillTank. To decrease the risk of consuming the wrong medication, PillTank identifies the pills being taken and ensures people, especially senior citizens, are correctly adhering to their treatment. To make PillTank simple, it is stripped down to its bare bones - a platform, camera, and an electronic screen. Emphasizing accessibility, accuracy, and speed, the user can simply place their pills on PillTank’s platform and a recognition algorithm will run. A detailed description of the pills will then be displayed on a large electronic screen. The history of scanned pills is then saved, which can provide family members or healthcare professionals insight as to what medication someone may be taking. While the initial form is a stand-alone device, PillTank can be expanded on to include voice recognition and an audio feedback system. Depending on the use case, this flexibility allows us to broaden our audience, all while ensuring that people stay safe and healthy by knowing what pills they are taking. Overall, PillTank removes the challenge of reading the fine print on pills by identifying them for the user, and allows users to consume their medication responsibly and safely

Shape and Text Imprint Recognition of Pill Image Taken with a Smartphone

학위논문 (석사)-- 서울대학교 대학원 공과대학 기계항공공학부, 2017. 8. 이건우.삶을 이롭게 하기 위해 수 많은 약들이 제조 · 판매되지만 이러한 약들은 오용되거나 남용될 경우 사람에게 치명적인 문제를 일으킬 수 있다. 인터넷과 스마트폰을 통해 알약을 검색하고 그에 대한 정보를 얻을 수 있으나, 모양과 색상, 글자를 직접 입력 해야하고 특수한 마커를 배경으로 사용해야하는 등 접근성이 낮다. 따라서 특수한 마커를 사용하지 않으면서도 알약 이미지로부터 글자 정보까지 획득할 필요가 있다. 본 논문에서는 스마트폰으로 촬영된 알약 영상에서 글자와 형상을 인식하는 방법을 제안한다. 알약이 포함된 영상에서 알약 영역을 특정 짓기 위해 Saliency Map을 이용한 뒤, 빛 효과와 그림자 효과를 제거한다. 이렇게 획득된 알약 영역에서 Zernike Moment를 통해 형상 정보를 얻는다. Gaussian Filter, Gradient Filter, Binarization을 통해 알약의 글자를 감싸는 박스를 추출하고, CNN Deep Learning을 통해 학습된 각인 글자 인식기를 사용하여 최종적인 글자 정보를 획득한다. 본 논문에서 제안하는 알고리즘을 테스트하기위해 NLM 데이터베이스의 알약 이미지 총 500개를 사용하였다. 또한, 1) Shape Matching Rate, 2) Text Box Detection Rate, 3) Character Recognition Rate, 4) Text Recognition Rate, 5) Recognition Success Rate로 총 5가지 항목에 대해 알고리즘을 평가하였다. 그 결과는 각각 75.5%, 87.5%, 0.786, 73%, 58.4%였으며, 기존의 알약 인식 알고리즘과 비교하였을 때, 상당히 개선된 결과를 얻었다.제 1 장 서론 1 제 2 장 관련연구 4 2.1 모양 또는 색상 등 일부만 인식 4 2.2 모양과 색상, 글자 모두 인식 5 제 3 장 제안된 인식 시스템 10 3.1 전체 시스템 개요 10 3.2 알약 주변부 획득 11 3.3 알약 영역 추출 13 3.4 형상 정보 획득 14 3.5 글자부 획득 19 3.6 글자 학습 및 인식 22 제 4 장 실험 및 결과 25 4.1 Database 25 4.2 Shape Matching Rate 26 4.3 Text Box Detection Rate 29 4.4 Character Recognition Rate 30 4.5 Text Recognition Rate & Recognition Success Rate 32 제 5 장 결론 34 참고문헌 35 Abstract 39Maste

Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection

Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i.e., samples from open-set anomaly classes), while effectively identifying the seen anomalies. Benefiting from the prior knowledge illustrated by the seen anomalies, current OSAD methods can often largely reduce false positive errors. However, these methods treat the anomaly examples as from a homogeneous distribution, rendering them less effective in generalizing to unseen anomalies that can be drawn from any distribution. In this paper, we propose to learn heterogeneous anomaly distributions using the limited anomaly examples to address this issue. To this end, we introduce a novel approach, namely Anomaly Heterogeneity Learning (AHL), that simulates a diverse set of heterogeneous (seen and unseen) anomaly distributions and then utilizes them to learn a unified heterogeneous abnormality model. Further, AHL is a generic framework that existing OSAD models can plug and play for enhancing their abnormality modeling. Extensive experiments on nine real-world anomaly detection datasets show that AHL can 1) substantially enhance different state-of-the-art (SOTA) OSAD models in detecting both seen and unseen anomalies, achieving new SOTA performance on a large set of datasets, and 2) effectively generalize to unseen anomalies in new target domains.Comment: 18 pages, 5 figure

Dynamics of learning and recent RX toward improving memory abilities

As an educator among educators, the writer is concerned with the intellectual potential of individuals viewed through their intellectual ability, and, presently, restricted to the function of memory. The writer is also deeply interested with the nature of research in several areas which encompass memory -- the key to educational improvement

Quality and safety assessment of sexual performance enhancement herbal medicines

Master'sMASTER OF SCIENC