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To describe a reproducible and easily available goat socket model for training of various oculoplastic operations, and to evaluate trainees' perception of this training model in terms of their learning progress and satisfaction.Goat sockets including orbital rim and eye with eyelids were harvested in form of a split-head model. Ophthalmology residents underwent individual surgical training using the goat socket model, supervised by an oculoplastic attending. Participants completed a questionnaire in form of a 5-point Likert Scale to evaluate their learning progress and satisfaction.Types of oculoplastic operations performed using the goat socket models, and participants' rating of their learning progress and satisfaction were reported.A wide range of oculoplastic operations including both eyelid and orbital operations could be simulated because of similarities of the goat eye model to the human eye anatomy. Fifteen ophthalmic trainees participated in surgical training using the goat eye model. All (100%) participants agreed that surgical simulation using the goat socket model increased their skills in surgical instrumentation and carrying out surgical steps, and their confidence in operating on patients. Most (87%) agreed the model resembled reasonably well compared to surgeries in human, and 93% would recommend training with the model to fellow resident ophthalmologists before operating on human patients.Oculoplastic surgical training using goat sockets is simple, readily available, and inexpensive. Trainee users showed promising feedback and positive learning progress using the goat socket model.

A 62-year-old woman presented with optic neuropathy, proptosis, and ocular dysmotility secondary to an osteolytic orbital tumor. Subsequent tests confirmed the diagnosis of multiple myeloma. Differential diagnoses of osteolytic orbital lesions were discussed.

We report a rare case of metastasis to the nasolacrimal sac from colorectal carcinoma in a 69-year old man. Despite tumor removal and endoscopic dacryocystorhinostomy with stenting, the tumor recurred 3 months later, which was treated with chemotherapy and radiotherapy. Five months later, the patient eventually died from respiratory failure secondary to malignant pleural effusion.

Orbital lesions in adults (VEINS: vascular, endocrine, inflammatory/infective, neoplastic, structural) range from a simple cyst to life-threatening disorder. They may cause ocular discomfort, visual dysfunction and/or periorbital disfigurement. A step-by-step evaluation starts with history-talking focusing on patients’ chief complaints, eliciting relevant ocular and orbital signs to arranging radiological, histological and systemic investigations. A multidisciplinary team involving ophthalmologists, radiologists, histopathologists, oncologists and neurological, plastic or ENT surgeons are often required to manage many challenging orbital conditions.

In this paper the structure of a vision-based object recognition system for classifying high tolerance engine parts is presented. This system could be divided into four basic levels: (1) data acquisition operation; (2) feature extraction algorithm; (3) classifier design; and (4) decision-making strategy. A mechano-optical arrangement in conjunction with a frame grabber and a software program are used to generate and capture the images of the components. Another software program based on the circular Hough transform algorithm is applied to the images for extracting the centre coordinates of the side profiles of the image as the features of the component. Then, a bell-shape membership function is generated using the extracted data. A unique feature matrix for each component could be defined from the corresponding membership functions. For object classification, a fuzzy cross-correlation algorithm is used for comparing the testing feature matrix with the reference feature matrix and obtained an aggregative relationship between the components.

The authors have developed a novel image sensor for continuous conditioned monitoring of high-precision tolerances of a complex automotive product. A novel mechanooptical arrangement has been designed and validated to capture the images/silhouettes of the components as input into a neural network designed on approximate reasoning architecture. The design is extensible to handle a large number of rules, and the speed of inference is almost independent of the number of rules.

An object recognition scheme for inspection of high tolerances in manufactured engine components has been developed and tested. A mechano-optical arrangement is used to generate and capture images of the components. A simple edge detection algorithm and an effective feature transformation plus indexing technique are applied to extract and analyse the features of the images. A classification scheme based on fuzzy membership functions is proposed. Some promising results of feature extraction, feature analysis and classification are shown.

Advances in image processing architecture have provided speed and inspection capabilities previously not attainable for machine vision applications. Methods for improving the quality of visual data have stored great interest. Image acquisition has become increasingly important for the analysis of complex scenes where grey scale, colour, depth, texture and/or motion information is present. In this paper, we illustrate our design of a prototype system for the diagnosis of high tolerances in machined or cast components that copes with uncertainty and performs approximate reasoning since information used in decision-making or reasoning processes in advanced manufacturing metrology could be uncertain, imprecise, or incomplete. In the design, we employ fuzzy logic based on fuzzy sets theory. Inference procedures that incorporate uncertainty are becoming more important in rule-based expert-like systems. The design is extensible to handle a large number of rules, and the speed of inference is almost independent of the number of rules.