MODUL PEMBELAJARAN PEMBANGUNAN BERKELANJUTAN (SUSTAINABLE DEVELOPMENT) ISLAM DAN LINGKUNGAN HIDUP

ABSTRAK MODUL PEMBELAJARAN PEMBANGUNAN BERKELANJUTAN (SUSTAINABLE DEVELOPMENT) ISLAM DAN LINGKUNGAN HIDUP Oleh : SUMA ELBITA Proses belajar mengajar, kehadiran media mempuyai arti yang amat penting karena kerumitan atau ketidak jelasan materi pembelajaran yang disampaikan akan disederhanakan dengan bantuan media. Modul dapat membantu mahasiswa memperoleh materi pembelajaran, karena dalam kegiatan belajar di perguruan tinggi menuntut kemandirian mahasiswa. Modul ini membahas tentang ”Pembangunan Berkelanjutan (Sustainable Development) Islam dan Lingkungan Hidup”. Materi yang akan dibahas pada modul ini yaitu : Pembangunan Berkelanjutan (Sustainable Development); Kampus Hijau (Green Campus) UIN Raden Intan Lampung; Penataan dan Infrastruktur yang Ideal; Energi dan Perubahan Iklim; Konservasi Air dan Upaya Penghematannya; Pengelolaan Limbah; Transportasi Ramah Lingkungan. Kita sebagai manusia mempunyai peran besar dalam menjaga lingkungan. Lingkungan hidup merupakan sumberdaya alam penopang kehidupan. Lingkungan memberikan segala sesuatu yang manusia butuhkan seperti oksigen, air dan lainnya. Allah SWT telah menciptakan bumi dan seisinya untuk kita nikmati dan kita jaga. Perlu adanya pembelajaran islam dan lingkungan hidup, guna memberikan kita pengetahuan dan arahan dalam sama-sama menjaga lingkungan hidup. Kata Kunci : Media, Modul, Pembangunan Berkelanjutan (Sustainable Development), Islam, Lingkungan Hidup

Efficient Processing of Corneal Confocal Microscopy Images. Development of a computer system for the pre-processing, feature extraction, classification, enhancement and registration of a sequence of corneal images.

Corneal diseases are one of the major causes of visual impairment and blindness worldwide. Used for diagnoses, a laser confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, ophthalmologists can extract clinical information on the state of health of a patient’s cornea. However, many factors impede ophthalmologists in forming diagnoses starting with the large number and variable quality of the individual images (blurring, non-uniform illumination within images, variable illumination between images and noise), and there are also difficulties posed for automatic processing caused by eye movements in both lateral and axial directions during the scanning process. Aiding ophthalmologists working with long sequences of corneal image requires the development of new algorithms which enhance, correctly order and register the corneal images within a sequence. The novel algorithms devised for this purpose and presented in this thesis are divided into four main categories. The first is enhancement to reduce the problems within individual images. The second is automatic image classification to identify which part of the cornea each image belongs to, when they may not be in the correct sequence. The third is automatic reordering of the images to place the images in the right sequence. The fourth is automatic registration of the images with each other. A flexible application called CORNEASYS has been developed and implemented using MATLAB and the C language to provide and run all the algorithms and methods presented in this thesis. CORNEASYS offers users a collection of all the proposed approaches and algorithms in this thesis in one platform package. CORNEASYS also provides a facility to help the research team and Ophthalmologists, who are in discussions to determine future system requirements which meet clinicians’ needs.The data and image files accompanying this thesis are not available online

Preparation of 2D sequences of corneal images for 3D model building

A confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, medical practioners can extract clinical information on the state of health of the patient's cornea. In this work we are addressing problems associated with capturing and processing these images including blurring, non-uniform illumination and noise, as well as the displacement of images laterally and in the anterior–posterior direction caused by subject movement. The latter may cause some of the captured images to be out of sequence in terms of depth. In this paper we introduce automated algorithms for classification, reordering, registration and segmentation to solve these problems. The successful implementation of these algorithms could open the door for another interesting development, which is the 3D modelling of these sequences

A fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy in corneal images

Diabetic Peripheral Neuropathy (DPN) is one of the most common types of diabetes that can affect the cornea. An accurate analysis of the nerve structures can assist the early diagnosis of this disease. This paper proposes a robust, fast and fully automatic nerve segmentation and morphometric parameter quantification system for corneal confocal microscope images. The segmentation part consists of three main steps. First, a preprocessing step is applied to enhance the visibility of the nerves and remove noise using anisotropic diffusion filtering, specifically a Coherence filter followed by Gaussian filtering. Second, morphological operations are applied to remove unwanted objects in the input image such as epithelial cells and small nerve segments. Finally, an edge detection step is applied to detect all the nerves in the input image. In this step, an efficient algorithm for connecting discontinuous nerves is proposed. In the morphometric parameters quantification part, a number of features are extracted, including thickness, tortuosity and length of nerve, which may be used for the early diagnosis of diabetic polyneuropathy and when planning Laser-Assisted in situ Keratomileusis (LASIK) or Photorefractive keratectomy (PRK). The performance of the proposed segmentation system is evaluated against manually traced ground-truth images based on a database consisting of 498 corneal sub-basal nerve images (238 are normal and 260 are abnormal). In addition, the robustness and efficiency of the proposed system in extracting morphometric features with clinical utility was evaluated in 919 images taken from healthy subjects and diabetic patients with and without neuropathy. We demonstrate rapid (13 seconds/image), robust and effective automated corneal nerve quantification. The proposed system will be deployed as a useful clinical tool to support the expertise of ophthalmologists and save the clinician time in a busy clinical setting

Endocarditis With Complete Heart Block

Gram-negative bacterial endocarditis causes 5% of all bacterial endocarditis. Among gram-negative bacteria, Klebsiella species are rare causes of native valve endocarditis. Klebsiella oxytoca is an extremely rare subspecies that can infrequently cause endocarditis and is associated with poor outcome. We report a case of Klebsiella oxytoca endocarditis in an elderly man who initially presented with stroke but later developed sepsis and heart block secondary to endocarditis