ڈھلتی سی شام کی جو ہوا چل پڑی تو پھر
اک اختتام کی جو ہوا چل پڑی تو پھر
دل کے محل میں چاہے دریچہ تو مت بنا
میرے ہی نام کی جو ہوا چل پڑی تو پھر
واعظ تو حبس وعظ سے جتنا بھی پیدا کر
مینا و جام کی جو ہوا چل پڑی تو پھر
تیرا خیال ہے کہ ہے یہ درد عارضی
درد دوام کی جو ہوا چل پڑی تو پھر
تو نے سمجھ لیا ہے کہ انمول ہے یہ حسن
اور سستے دام کی جو ہوا چل پڑی تو پھر
حاکم تجھے ہے خوف جو سچ کے نظام سے
گر اس نظام کی جو ہوا چل پڑی تو پھر
The article attempts to analyze the religious and legal practices inside Pakistan regarding the issue of the kitabiyyah mother and ḥaḍanah of any Muslim child. The discussion primarily addresses the major issues emphasizing kitabiyyah mother and her relatives’ right to retain the custody of the Muslim child, and duration of ḥaḍanah under the supervision of kitabiyyah mother in shari‘ah and Pakistani Family Laws. Furthermore, it argues that kitabiyyah mother is permitted to raise the Muslim child according to her faith under both Islamic and Pakistani Family Laws. The article is delimited to the opinions of four Sunni schools of thoughts, Statute law, and Case laws. Nonetheless, in-depth comparative analysis has been carried out in most persuasive way to examine the rules related to kitabiyyah mother in custody of Muslim child after divorce under both Islamic Law, and Case Laws in Pakistan. Additionally, the existing similarities and differences consequent to religious differences have also been appropriately figured out to point out plausible way forward to address prevailing schism. Contradictions between the legal practices and Islamic law need keen attention Islamic and legal scholars to carefully craft to harmonize both in the best interest of child.
The advancement in microscopic imaging techniques results in the generation of a large amount of high quality data in no time. The accurate, real time and autonomous analysis of this data is crucial for the theoretical biomedical research and clinical diagnosis. A lot of vigorous attempts are dedicated for the evolution of computer aided techniques, which improve human diagnosis by increasing efficiency, decreasing variability in the observations and reducing the human effort on labelling and classifying images. Among such determined attempts, histology image classification is one of the most significant fields due to its extensive application in pathological diagnosis such as tumor/cancer diagnosis. Inherent heterogeneous nature and random spatial intensity differences of histopathology images make the histology tissue classification a complex task. In this thesis four novel, robust and adaptive frameworks are proposed for automated and correct classification of histopathology images. The goal of this research is to achieve the expert pathologists’s diagnosis by catering inherent complexity and prevailing the variations in the opinion of different pathologists. The key contributions of this reseach are: First, a histopathological classification problem is explored from all the perspectives by performing pattern analysis at image level and cell level individually and collectively. The first proposed framework is an abstract feature based framework which performs image-level analysis to capture global texture information. The second framework performs cell-level analysis to get nuclei structure and texture. The third and fourth frameworks perform cell-level and image-level analysis to get nuclei structure and image global texture. Second, the exploration of RGB colour space is preferred to mimic the pathologists’ diagnosis process. The imperative role of RGB color channels is investigated in histopathology image classification by extracting nuclear and global image features across these color channels. Third, instead of analyzing various colour spaces a number of feature measures are explored from spatial and frequency domain to encounter maximum diversity. The individual and combined effect of a large number of statistical, structural and spectral feature measures are analyzed including co-occurrence matrices, run-length matrices, local binary patterns with Fourier transforms, morphology features and intensity features. Fourth, an extensive investigation of rank-based feature selection schemes is performed and proved that elitism is not an optimal strategy for feature selection in histopathology image classification. An abstract feature i.e. an optimal combination of functionally collaborating features having implicit linkages is identified based on classification accuracy through evolutionary search process. Fifth, a number of classifiers are explored and an automatic selection of parameters of classification model (classifier and classifier’s parameters) is performed through Genetic Algorithm based evolutionary technique. The experimentation is performed on images of grade-I benign meningioma four subtypes (meningothelial, fibroblastic, transitional and psammomatous) and pre-invasive breast lesions four classes (usual ductal hyperplasia (UDH) and three nuclear grades of ductal carcinoma in situ (DCIS)). The proposed frameworks achieved the promising classification results for four meningioma subtypes and breast lesions grades. In most of the cases, optimal sets of features obtained from the combination of three color channels and classified through linear support vector machine classifier presented the highest classification accuracy. The extraction of nuclear texture in spatial and frequency domain presented promising classification results.